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Park, Byeong-Ju, and Jae-Heung Lee. "High Efficient Viola-Jones Detection Framework for Real-Time Object Detection." Journal of IKEEE 18, no. 1 (2014): 1–7. http://dx.doi.org/10.7471/ikeee.2014.18.1.001.
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Egorov, A. D. "Algorithm for optimization of Viola–Jones object detection framework parameters." Journal of Physics: Conference Series 945 (January 2018): 012032. http://dx.doi.org/10.1088/1742-6596/945/1/012032.
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Nivodhini, M. K., B. Rajesh, S. Venkatesh Babu, et al. "Emotion Detection Through Facial Expression Recognition Using the Viola-Jones Algorithm." Journal of Neonatal Surgery 14, no. 5 (2025): 258–64. https://doi.org/10.52783/jns.v14.2931.
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Given the critical role that facial expressions play in human connection and communication, facial expression detection and recognition have attracted a lot of interest recently. The numerous uses of facial expression detection in a variety of industries, including virtual reality, intelligent tutoring systems, healthcare, and data-driven animation, are largely responsible for this spike in interest. The primary objective of facial expression recognition is to precisely recognise people's emotional states from a variety of face photographs. These states include anger, contempt, disgust, fear, happiness, sadness, and surprise. This research focuses on the detection and recognition of facial expressions using the Viola-Jones algorithm. The Viola-Jones method provides a strong framework for evaluating facial features and identifying nuanced expressions across various scales and orientations. It is well-known for its efficiency and effectiveness in object detection. Facial expression detection and recognition are made possible by the Viola-Jones algorithm, which also improves the functionality of other technological systems and advances human-computer interaction. With its ability to accurately and efficiently analyse human emotions, the Viola-Jones algorithm has the potential to revolutionise a wide range of industries. This study intends to investigate the application of this algorithm in the recognition of facial expressions.
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Voronova, Larissa V., and Elena V. Panisheva. "ON THE QUESTION ON METHOD SELECTION OF THE EDGE DETECTION AND GRAPHIC OBJECT RECOGNITION APPLIED TO THE TASK OF LICENCE PLATE IDENTIFICATION." Technologies & Quality 56, no. 2 (2022): 46–50. http://dx.doi.org/10.34216/2587-6147-2022-2-56-46-50.
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The method of the edge detection and graphic object recognition – licence plate is presented in this article. The development and use of an automatic car number recognition system is an urgent task, as it allows you to control the access of a car to a closed protected area without the participation of an operator. The article presents a comparative analysis of the quality and efficiency of various methods (Viola–Jones object detection framework, Canny edge detector, Sobel operator). The authors proposed a modification of the method for determining boundaries within the framework of the problem being solved, quantified the accuracy of recognition.
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Mejalin Arno B, Vinothagan J, Arun Kumar R, and Gowthami V. "Smart Assistive System for the Visually Impaired." international journal of engineering technology and management sciences 7, no. 4 (2023): 68–76. http://dx.doi.org/10.46647/ijetms.2023.v07i04.014.
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Recent advances in innovation have made ordinary people's lives richer, simpler, and easier. Many people suffer from vision impairment, which causes many difficulties in their daily work, according to World Health Organization (WHO) statistics. Our goal is to develop a framework that is unobtrusive, safe, portable, and versatile in order to assist blind people in their daily lives. It intends to develop an effective system for assisting the visually impaired with obstacle detection and scene classification. The Raspberry-Pi 3B+ and a camera module are used in the proposed method. It photographs the scene and then preprocesses the images with Viola-Jones and Yolov5 object detection algorithms. The aforementioned techniques are used to detect people and objects. We also used a sound system that speaks when an object or person is detected. The presented study implements simple computations and detects obstacles with high efficiency. Unlike other frameworks, this framework is lightweight and easy to transport
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Ismael, Khansaa Dheyaa, and Stanciu Irina. "Face recognition using Viola-Jones depending on Python." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1513–21. https://doi.org/10.11591/ijeecs.v20.i3.pp1513-1521.
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In this paper, the proposed software system based on face recognition the proposed system can be implemented in the smart building or any VIP building need security interring in general. The human face will be recognized from a stream of pictures or video feed, this technology recognizes the person according to the specific algorithm, the algorithm that employed in this paper is the Viola–Jones object detection framework by using Python. The task of the proposed facial recognition system consists of two steps, the first one was detected the human face from live video using the webcamera in the computer, and the second step recognizes if this face allowed to enter the building or not by comparing it with the existing database, the two steps depending on the OpenCV python by importing cv2 method for detecting the human face, the frames can be read or written to file with the cv2.imread and cv2.imwrite functions respectively Finally, this proposed software system can be used to control access in smart buildings as a rule and the advancement of techniques connected around there, Providing a security system is one of the most important features must be achieved in the smart buildings, this proposed system can be used as an application in a smart building as a security system. Face recognition is one of the most important applications using today for practical facial recognition, The proposed software system, depending on using OpenCV (Open Source Computer Vision) is a popular computer vision library, in 1999 this library started by Intel. The platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. OpenCV 2.3.1 now comes with a programming interface to C, C++, Python, and Android. OpenCV library of python, the three algorithms that will be used in this proposed system. The currently available algorithms are: Eigenfaces → createEigenFaceRecognizer (), Fisherfaces → createFisherFaceRecognizer (), Local Binary Patterns Histograms → createLBPHFaceRecognizer (), Finally the proposed system provide entering to the building just for the authorized person according to face recognition algorithem
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Milutinovici, S. "Face extraction and clustering with Viola - Jones object detection framework and T-SNE dimensionality reduction." Scientific Bulletin of Naval Academy XXVI, no. 2 (2023): 100–107. http://dx.doi.org/10.21279/1454-864x-23-i2-012.
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We investigate the possibility to use Viola-Jones [1] object detection framework through a multi-model approach to build a face extraction pipeline that will be used in video appearance tagging. Although deep convolutional neural networks have surpassed previous algorithms in performance [2], Haar Cascades needs much lower memory than CNN, does not require specialized hardware, and has lower storage requirements. Most videos will show the same face more than once, at least a few close-ups that are full frontal and well lit. We need an efficient system that will extract the best appearances. This study shows the pre-trained model selection, the fine-tuning of run-time parameters and the test. After selection of models for faces, eyes, mouths and noses and testing the right runtime parameters we were able to establish a procedure that will avoid any false positives and will produce a set of well defined faces.tart your abstract here
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Dheyaa Ismael, Khansaa, and Stanciu Irina. "Face recognition using viola-jones depending on python." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1513. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1513-1521.
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<p>In this paper, the proposed software system based on face recognition the proposed system can be implemented in the smart building or any VIP building need security interring in general, The human face will be recognized from a stream of pictures or video feed, this technology recognizes the person according to the specific algorithm, the algorithm that employed in this paper is the Viola–Jones object detection framework by using Python. The task of the proposed facial recognition system consists of two steps, the first one was detected the human face from live video using the webcamera in the computer, and the second step recognizes if this face allowed to enter the building or not by comparing it with the existing database, the two steps depending on the OpenCV python by importing cv2 method for detecting the human face, the frames can be read or written to file with the cv2.imread and cv2.imwrite functions respectively Finally, this proposed software system can be used to control access in smart buildings as a rule and the advancement of techniques connected around there, Providing a security system is one of the most important features must be achieved in the smart buildings, this proposed system can be used as an application in a smart building as a security system. Face recognition is one of the most important applications using today for practical facial recognition, The proposed software system, depending on using OpenCV (Open Source Computer Vision) is a popular computer vision library, in 1999 this library started by Intel. The platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. OpenCV 2.3.1 now comes with a programming interface to C, C++, Python, and Android. OpenCV library of python, the three algorithms that will be used in this proposed system. The currently available algorithms are:</p><p>Eigenfaces → createEigenFaceRecognizer()</p><p>Fisherfaces → createFisherFaceRecognizer()</p><p>Local Binary Patterns Histograms → createLBPHFaceRecognizer()</p>Finally the proposed system provide entering to the building just for the authorized person according to face recognition algorithem.<p> </p>
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Miyazaki, Shintaro. "Take Back the Algorithms! A Media Theory of Commonistic Affordance." Media Theory 3, no. 1 (2019): 269–86. http://dx.doi.org/10.70064/mt.v3i1.957.
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This essay critiques the ‘black-boxing’ of many computational processes, which are argued to result in a kind of ‘unaffordability’ of algorithms. By engaging with current theoretical debates on ‘commoning’ – signifying a non-profit-oriented, solidarity-based approach to sharing, maintaining, and disseminating knowledge and experience – the essay offers a formulation of commonistic affordance in algorithmic contexts. Through the discussion of widely used computational tools such as the Viola-Jones object detection framework, radical steps towards a ‘making affordable’ of algorithms are outlined, and the widespread corporate propertisation of computation processes is contrasted with a speculative vision of algorithmic commoning.
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Siddiqua, Maria, and Muhammad Furqan Zia. "A Robust Framework for 2D Human Face Reconstruction from Half-Frontal Views in Low-Quality Surveillance Footage." International Journal of Emerging Engineering and Technology 3, no. 2 (2024): 13–18. https://doi.org/10.57041/z5e98x92.
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This paper proposes a robust framework for reconstructing 2D human facial images from half-frontal views, primarily captured under low-quality surveillance conditions. A custom MATLAB-based Graphical User Interface (GUI) is developed to support the complete pipeline, including frame extraction, enhancement, and face reconstruction. Representative frames are extracted and enhanced for video inputs using one of three techniques: histogram equalization, contrast stretching, or logarithmic transformation. Reconstruction involves detecting a single eye from the half-frontal image, followed by horizontal flipping and concatenation to generate a symmetric full-frontal face. The reconstructed faces are validated using the Viola-Jones object detection algorithm to confirm the presence and alignment of facial features. Quantitative evaluation uses the Structural Similarity Index (SSIM) and Jaccard Index (JI) to measure image quality and geometric accuracy. The proposed method is tested on publicly available datasets and a custom-designed dataset reflecting real-world surveillance challenges such as low resolution and poor illumination. Experimental results demonstrate that the framework delivers accurate and visually coherent reconstructions with low computational overhead, making it suitable for real-time surveillance and facial analysis applications.
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Snehitha, Kanaparthi. "Facial Expression Recognition with Appearance Based Features of Facial Landmarks." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3343–48. http://dx.doi.org/10.22214/ijraset.2021.35702.
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Artificial intelligence technology has been trying to bridge the gap between humans and machines. The latest development in this technology is Facial recognition. Facial recognition technology identifies the faces by co-relating and verifying the patterns of facial contours. Facial recognition is done by using Viola-Jones object detection framework. Facial expression is one of the important aspects in recognizing human emotions. Facial expression also helps to determine interpersonal relation between humans. Automatic facial recognition is now being used very widely in almost every field, like marketing, health care, behavioral analysis and also in human-machine interaction. Facial expression recognition helps a lot more than facial recognition. It helps the retailers to understand their customers, doctors to understand their patients, and organizations to understand their clients. For the expression recognition, we are using the landmarks of face which are appearance-based features. With the use of an active shape model, LBP (Local Binary Patterns) derives its properties from face landmarks. The operation is carried out by taking into account pixel values, which improves the rate of expression recognition. In an experiment done using previous methods and 10-fold cross validation, the accuracy achieved is 89.71%. CK+ Database is used to achieve this result.
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Al-Anssari, Haitham Asaad, Ikhlas Abdel-Qader, and Maureen Mickus. "Monitoring System for Persons With Alzheimer's Disease via Video-Object Tracking." International Journal of Mobile Devices, Wearable Technology, and Flexible Electronics 9, no. 2 (2018): 18–36. http://dx.doi.org/10.4018/ijmdwtfe.2018070102.
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This article presents a framework for a food intake monitoring system intended for use with persons with Alzheimer's disease and other dementias. Alzheimer's disease has a significant impact on the individual's ability to perform their daily activities including eating. Providing assistance with feeding is a major challenge for caregivers, including a significant time commitment. We present a vision-based system that tracks moving objects, such as the hand, using a combined optical flow and skin region detection algorithms. Skin detection is implemented using two different methods. Hue, saturation, and value (HSV) color space, which is on separation of the illuminance component from chrominance one as the first method and skin color information is extracted from subject's face detected using Viola-Johns algorithm for the second method. Once face and other moving skin regions are detected, bounding boxes are created and used to track all moving regions over the video frames, recognizing eating behavior or the lack of it. Based on experimental results the proposed method using optical flow and skin regions segmentation using HSV color detects the hand to mouth eating motion with 92.12% accuracy. The optical flow and skin region segmentation based on face color information achieves a higher accuracy of 94.29%.
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Stevanović, Dušan. "OBJECT DETECTION USING VIOLA-JONES ALGORITHM." Knowledge International Journal 28, no. 4 (2018): 1349–54. http://dx.doi.org/10.35120/kij28041349d.
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In this paper it has been described and applied method for detecting face and face parts in images using the Viola-Jones algorithm. The work is based on Computer Vision Systems, artificial intelligence that deals with the recognition of two-dimensional or three-dimensional objects. When Cascade Object Detector script is trained, multimedia content is assigned for recognition. In this work the content will be in the form of an image, where the program will have the task of recognizing the objects in the images, separating the parts of the images in the head area, and on each discovered face, separately mark the area around the eyes, nose and mouth.Algorithm for detection and recognition is based on scanning and analyzing front part of human head. Common usage of face detection and recognition can be find in biometry, photography, on autofocus option which is implemented in professional photo cameras or on smiling detectors (Keller, 2007). Marketing is also popular field where face detection and recognition can be used. For example, web cameras that are implemented in TVs, can detect every face in near area. Calculating different type of algorithms and parameters, based on sex, age, ethnicity, system can play precisely segmented television commercials and campaigns. Example of that kind of systems is OptimEyes. (Strasburger, 2013)In other words, every algorithm that has as its main goal to detect and recognize face from image, should give as a feedback information, is there any face and if answer is positive, where is its location on image. In order to achieve acceptable performances, algorithm should minimize false recognitions. These are the cases when the algorithm ignores and does not recognize the real object from the image, and vice versa, when the wrong object is recognized as real. One of the algorithms that is frequently applied in this area of research is the Viola-Jones algorithm. This algorithm is functional in real time, meaning that besides detection, it is also possible to adjust the ability to monitor faces from video material.In this paper, the problem that will be analyzed is facial image detection. Man can do this task in a very simple way, but to do the same with a computer, it is necessary to have a range of precise and accurate information, formulas, methods and techniques. In order to maximize the precision of recognizing the face of the image using the Viola-Jones algorithm, it is desirable that the objects in the images are completely face-to-face with the image-taking device, which will be shown through experiments.
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Cherednyk, Oleksandr, and Elżbieta Miłosz. "Object recognition on video from camera to computer." Journal of Computer Sciences Institute 8 (November 30, 2018): 215–19. http://dx.doi.org/10.35784/jcsi.682.
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The goal is to determine the effectiveness of object detection in a video using the camera for the computer. In the course of work studied and described the main methods of recognition of objects in the image, namely the use of artificial neural networks and techniques of Viola-Jones. For the study, based on the method of Viola-Jones, implemented the application for object recognition in video, as this method is effective for solving this problem. With this application, a study was conducted to determine the effectiveness of the method of viola-Jones to detect objects in the video.
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Andryani, Nur Afny C. "Study of Viola Jones Face Detection on Color Image based on Skin Pigmentation Level." Jurnal Elektro dan Mesin Terapan 1, no. 1 (2015): 44–52. http://dx.doi.org/10.35143/elementer.v1i1.16.
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Automatic face detection has been very complex and challenging research topic due to the complexity of faces’ characteristics that is not rigid object. There have been many works on proposing robust algorithm on image detection. Many researcher use Viola Jones algorithm as their initial point and benchmark. The Viola-Jones face detection itself is the most popular and recent applicable algorithm that has been developed since 2004 by Paul Jones from Microsoft R&D and its co-inventor, Michael J. Jones from Mitsubishi R&D. Many previous works present the study on the Viola Jones algorithm subject to frontal face with no consideration on the skin pigmentation level. This paper presents study on The Viola Jones performance on color image that consider skin pigmentation level. To indicate the skin pigmentation level, the L* element on CIELAB color space is used. The skin pigmentation level is clustered into dark skin, brown skin and fair skin. The simulation result show that the Viola Jones performance tends to decrease when the skin pigmentation getting high (dark skin). Some hypotheses test had been done to support the claim.
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Rahmiati, Ambiyar, Rika Melyanti, and Eka Sabna. "Detection Of Human Face Targets with Viola-Jones Method." JAIA - Journal of Artificial Intelligence and Applications 2, no. 1 (2021): 9–16. http://dx.doi.org/10.33372/jaia.v2i1.777.
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This study aims to implement Viola Jones into a simple face detection system by utilizing the existing library in Open CV and using the Python programming language. In this study, there were 10 sample images consisting of 8 images of human faces and 2 images of animals. The results of the study were 9 images were successfully detected and 1 image was less precise in determining the face. The system can detect the presence of several (more than one) faces in an image. The system can also detect an object that resembles a face when the object has the same contour as a human face, for example, a face on a statue.
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Arlazarov, V. V., Ju S. Voysyat, D. P. Matalov, D. P. Nikolaev, and S. A. Usilin. "Evolution of the Viola-Jones Object Detection Method: A Survey." Bulletin of the South Ural State University. Series "Mathematical Modelling, Programming and Computer Software" 14, no. 4 (2021): 52–23. http://dx.doi.org/10.14529/mmp210401.
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Guslianto, Safrida Ika, Khairunnas Khairunnas, Tachiyya Nailal Khusna, and Miftahul Jannah. "Real Time Mask Detection Using Viola Jones Method." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 12, no. 1 (2024): 119–26. http://dx.doi.org/10.33558/piksel.v12i1.9028.
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The coronavirus disease (COVID-19) pandemic requires us to wear masks when doing direct contact with other people. The use of masks is mandatory in some places to break the chain of the spread of the COVID-19 virus. The spread of this virus occurs through the respiratory tract. Wearing masks is a form of our efforts to suppress the spread of this virus. This study detects human face objects in a room to determine faces that use masks and faces without using masks. The research was conducted using the Viola Jones Method in the OpenCV-Python library. This paper resulted in a good accuracy of the tests carried out to obtain 95.6% results. This detection agent has been able to perform face recognition with the condition of using a mask and the face without using a mask. On the display screen will show the face recognition accuracy in real time of the detected face object.
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Gollapalli, Geetha Siva Srinivas, Yaswanth Chowdary Thotakura, Shalom Raja Kasim, and Kalyan Kumar Doppalapudi. "Multi Object Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (2023): 1222–29. http://dx.doi.org/10.22214/ijraset.2023.55817.
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Abstract: In the realm of computer vision, the ability to accurately detect and comprehend objects within images and videos is of paramount importance. This research is dedicated to advancing the field of object detection, a critical component of computer vision, with a particular focus on leveraging Convolutional Neural Networks (CNNs) to enhance accuracy. CNNs have revolutionized object recognition tasks, outperforming traditional methods like Viola-Jones, SIFT, and HOG. The study explores the underlying architecture of CNNs, elucidating how convolution, pooling, and flattening layers enable efficient image processing and object identification. Object detection holds immense practical significance, spanning applications such as autonomous vehicles, surveillance, and medical imaging. By delving into the intricacies of CNNs and their role in object detection, this research contributes to the ongoing evolution of computer vision, promising advancements in diverse sectors of industry and technology.
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Putri, Rizka Eka, Tekad Matulatan, and Nurul Hayaty. "Sistem Deteksi Wajah Pada Kamera Realtime dengan menggunakan Metode Viola Jones." Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan 8, no. 1 (2019): 30–37. http://dx.doi.org/10.31629/sustainable.v8i1.526.
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In general, human are given the mind and mind to be able to determine or be able to didtinguish individuals who appear either human, animal, plant, and other objects that are known or unknown. And it is possible for human to recognize these object from their sight and from their brain memory. Especially on the human face, human can recognize whether the object is human or not human, and can recognize the object very well through his own eyes.face detection system in human becomes very important in the development of science of digital image processing. The research has been done with many advantages and disadvantages. From a face many information features that can be read, such as eyes, nose, and mouth. The detection system uses Viola Jones method as an object detection method. The Viola Jones method is known to have considerable Speed and accuracy as it combines several concepts (Haar feature, Integral image, Adaboost, Cascade classifier) into a main method for detecting objects.Based on tests conducted on face identification under conditions that may affect face detection results, the results show an accuracy of 67,6 % to detect the face.
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Et. al., Nouria Kaream Khoorshed,. "Car Surveillance Video Summarization Based On Car Plate Detection." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 1132–44. http://dx.doi.org/10.17762/turcomat.v12i6.2431.
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Today, video is a common medium for sharing information. Navigating the internet to download a certain form of video, it takes a long time, a lot of bandwidth, and a lot of disk space. Since sending video over the internet is too costly, therefore video summarization has become a critical technology. Monitoring vehicles of people from a security and traffic perspective is a major issue. This monitoring depends on the identification of the license plate of vehicles. The proposed system includes training and testing stages. Training stage comprises: video preprocessing, Viola-Jones training, and Support Vector Machine (SVM) optimization. Testing stage contains: test video preprocessing, car plate (detection, cropping, resizing, and grouping detecting test car plate, feature extraction using HOG feature. The total time of local recorded videos is (19.5 minutes), (15.5 minutes) for training, and (4 minutes) for testing. This means, (79.5%) for training and (20.5%) for testing. The proposed video summarization has got maximum accuracy of (86%) by using Viola-Jones and SVM by reducing the number of original video frames from (7077) frames to (1200) frames. The accuracy of the Viola-Jones object detection process for training 700 images is (97%). The accuracy of the SVM classifier is (99.6%).
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Archna, Devee*1 &. Dr. Pankaj Sharma2. "AUTOMATIC FACE DETECTION USING VIOLA-JONES FOR SECURITY PURPOSE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 574–79. https://doi.org/10.5281/zenodo.1228619.
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This is a project to develop an e-mail application using HCI features helping users check their mailbox easier. It is consist of a network module to develop a standalone email application to ask user home security. a face recognition module Face recognition algorithm and pick the fastest one to avoid lag and use it as a security level of application. This paper describes the technique for real time human face detection and tracking using a modified version of the algorithm suggested by Paul viola and Michael Jones. The paper starts with the introduction to human face detection and tracking, followed by apprehension of the Vila Jones algorithm and then discussing about the implementation in real video applications. Viola Jones algorithm was based on object detection by extracting some specific features from the image. We used the same approach for real time human face detection and tracking. Simulation results of this developed algorithm shows the Real time human face detection and tracking supporting up to 50 human faces. This algorithm computes data and produce results in just a mere fraction of seconds.
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Ridwan, Ahmad, Rian Ferdian, Jumadi Mabe Parenreng, and M. Udin Harun Al Rasyid. "Realtime Face Recognition System with Viola-Jones and Local Binary Pattern Histogram (LBPH) Method." Journal of Engineering and Science Application 1, no. 2 (2024): 33–41. https://doi.org/10.69693/jesa.v1i2.12.
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The face is one of the body parts that a detection system can detect. This is what underlies the existence of face detection. One technology that uses face identification is biometric identification. The Viola-Jones method determines whether an object is a face by extracting features in a face image and classifying it to decide whether or not it is a face. However, the Viola-Jones method has the disadvantage that it can only detect human faces. This research will combine the Viola-Jones method to recognize human faces with the Local Binary Pattern Histogram (LBPH) method. The result is that the system can detect and identify up to two human faces facing forward, sideways, up, and down for the database. An accuracy calculation is also added to measure the accuracy of face recognition after the database is retrieved and trained. This average percentage of correctness is taken from comparing the predicted face to be recognized with the face that will be recognized. The result will be compared again with the number of photos taken during the recognition process and multiplied by 100%.
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Imanuddin, Imanuddin, Fachrid Alhadi, Raza Oktafian, and Ahmad Ihsan. "Deteksi Mata Mengantuk pada Pengemudi Mobil Menggunakan Metode Viola Jones." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 18, no. 2 (2019): 321–29. http://dx.doi.org/10.30812/matrik.v18i2.389.
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Computer Vision is one of the branches of Image processing science that allows a combination of human beings, such as identifying an object like an eye and taking a decision. Many of the face detection systems use the Viola Jones method as an object detection method. The method of Viola Jones is known by having high speed and accuracy because it is useful to combine several concepts such as (Haar Features, Integral Image, AdaBoost, and Cascade Classifier) into a major method for detecting objects. The programming language used in this study uses the MATLAB programming language to facilitate the process of creating the system. The research aims to implement Viola Jones into a simple eye-sensing drowsiness system by utilizing the existing libraries in the MATLAB programming language. Once the system is completed, a system test is performed against the detected drowsiness detection characteristics. This eye drowsiness detection system aims to determine if the car rider is sleepy or not when driving with an input in the form of eye detection taken using a digital camera and then inserted into a language Programming GUI Matlab where the value is taken binary eyes, sleepy eyes and not sleepy that will be a reference that will be processed later so that it can produce the output of a warning sound to the rider of the sleepy car vehicle or not The sleepy automatically. The testing of the program gained an amount detected 7 eyes of 10 eyes by using BW 0255 level which is useful to accelerate a program to detect sleepy eyes.
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Senjaya, Benny, Alexander A. S. Gunawan, and Jerry Pratama Hakim. "Pendeteksian Bagian Tubuh Manusia untuk Filter Pornografi dengan Metode Viola-Jones." ComTech: Computer, Mathematics and Engineering Applications 3, no. 1 (2012): 482. http://dx.doi.org/10.21512/comtech.v3i1.2447.
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Information Technology does help people to get information promptly anytime and anywhere. Unfortunately, the information gathered from the Internet does not always come out positive. Some information can be destructive, such as porn images. To mitigate this problem, the study aims to create a desktop application that could detect parts of human body which can be expanded in the future to become an image filter application for pornography. The detection methodology in this study is Viola-Jones method which provides a complete framework for extracting and recognizing image features. A combination of Viola-Jones method with Haar-like features, integral image, boosting algorithm, and cascade classifier provide a robust detector for the application. First, several parts of the human body are chosen to be detected as the data training using the Viola-Jones method. Then, another set of images (similar body parts but different images) are run through the application to be recognized. The result shows 86.25% of successful detection. The failures are identified and show that the inputted data are completely different with the data training.
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Imran, Raza Khan, and Danish Mohammad. "REAL-TIME FACE DETECTION AND TRACKING FOR SECURITY SYSTEM." International Journal of Advances in Engineering & Scientific Research Vol.4, Issue 3, May-2017 (2017): pp 30–35. https://doi.org/10.5281/zenodo.806203.
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This is a project to develop an e-mail application using HCI features helping users check their mailbox easier. It is consist of a network module to develop a standalone email application to ask user home security. a face recognition module Face recognition algorithm and pick the fastest one to avoid lag and use it as a security level of application. This paper describes the technique for real time human face detection and tracking using a modified version of the algorithm suggested by Paul viola and Michael Jones. The paper starts with the introduction to human face detection and tracking, followed by apprehension of the Vila Jones algorithm and then discussing about the implementation in real video applications. Viola Jones algorithm was based on object detection by extracting some specific features from the image. We used the same approach for real time human face detection and tracking. Simulation results of this developed algorithm shows the Real time human face detection and tracking supporting up to 50 human faces. This algorithm computes data and produce results in just a mere fraction of seconds. <strong><em>Keywords</em></strong><em>- Face recognition; Email; Interactive; Blob detection,</em> Human face detection, Integral Image
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Kumar, P. Dinesh, and B. Rosiline Jeetha. "CANNY EDGE DETECTION AND CONTRAST STRETCHING FOR FACIAL EXPRESSION DETECTION AND RECOGNITION USING MACHINE LEARNING." ICTACT Journal on Image and Video Processing 12, no. 2 (2021): 2559–69. http://dx.doi.org/10.21917/ijivp.2021.0363.
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Facial expression recognition in the world is challenging due to various unconstrained conditions. Although existing facial expression classifiers have been almost perfect for analyzing constrained frontal faces, they fail to perform well on partially occluded faces common in the wild. This paper deals with Facial expression detection and recognition through the Viola-jones algorithm and HCNN using the LSTM method to avoid those challenges in recent work. For face detection, basically, utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and it helps to perform the feature selection through the whale optimization algorithm, once after compression and further, it minimizes the feature vector given into the HCNN and LSTM model for efficiently identifying the facial expression. In existing work, the feature extraction stage exact finding of the corner becomes a very difficult task, to solve this issue need to use a corner detection algorithm to enhance the feature extraction (corner points) from the face image. One of the main drawbacks of WOA is that it is not good at exploring the search space. To overcome those issues, the work introduced an improved framework for facial image recognition. In which first edges are detected using the canny edge detection operator. Then improved linear contrast stretching is used for image enhancement. Then in feature extraction, the author proposes Hybrid SIFT with Double d-LBP (Dd-LBP) to obtain the features that are illumination and pose independent. For face detection, utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and significant features are selected by using self-learning chicken swarm optimization, it minimizes the feature vector is given into the Hybrid HCNN and LSTM model for efficiently identifying the facial expression. Experimental results demonstrate the efficiency of the proposed work in terms of accuracy, precision, recall and f-measure.
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Imran, Raza Khan, and Danish Mohammad. "REAL-TIME FACE DETECTION AND TRACKING FOR SECURITY SYSTEM." International Journal of Advances in Engineering & Scientific Research 4, no. 3 (2017): 30–35. https://doi.org/10.5281/zenodo.10775970.
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<strong>ABSTRACT</strong><strong>: </strong> This is a project to develop an e-mail application using HCI features helping users check their mailbox easier. It is consist of a network module to develop a standalone email application to ask user home security. a face recognition module Face recognition algorithm and pick the fastest one to avoid lag and use it as a security level of application. This paper describes the technique for real time human face detection and tracking using a modified version of the algorithm suggested by Paul viola and Michael Jones. The paper starts with the introduction to human face detection and tracking, followed by apprehension of the Vila Jones algorithm and then discussing about the implementation in real video applications. Viola Jones algorithm was based on object detection by extracting some specific features from the image. We used the same approach for real time human face detection and tracking. Simulation results of this developed algorithm shows the Real time human face detection and tracking supporting up to 50 human faces. This algorithm computes data and produce results in just a mere fraction of seconds. <strong><em>Keywords</em></strong><em>- Face recognition; Email; Interactive; Blob detection,</em><strong> </strong>Human face detection, Integral Image
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Choi, Cheol-Ho, Joonhwan Han, Hyun Woo Oh, Jeongwoo Cha, and Jungho Shin. "EOS: Edge-Based Operation Skip Scheme for Real-Time Object Detection Using Viola-Jones Classifier." Electronics 14, no. 2 (2025): 397. https://doi.org/10.3390/electronics14020397.
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Machine learning-based object detection systems are preferred due to their cost-effectiveness compared to deep learning approaches. Among machine learning methods, the Viola-Jones classifier stands out for its reasonable accuracy and efficient resource utilization. However, as the number of classification iterations increases or the resolution of the input image increases, the detection processing speed may decrease. To address the detection speed issue related to input image resolution, an improved edge component calibration method is applied. Additionally, an edge-based operation skip scheme is proposed to overcome the detection processing speed problem caused by the number of classification iterations. Our experiments using the FDDB public dataset show that our method reduces classification iterations by 24.6157% to 84.1288% compared to conventional methods, except for our previous study. Importantly, our method maintains detection accuracy while reducing classification iterations. This result implies that our method can realize almost real-time object detection when implemented on field-programmable gate arrays.
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E. Widjaja, Andree, Hery Hery, and David Habsara Hareva. "The Office Room Security System Using Face Recognition Based on Viola-Jones Algorithm and RBFN." INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi 5, no. 1 (2021): 1–12. http://dx.doi.org/10.29407/intensif.v5i1.14435.
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The university as an educational institution can apply technology in the campus environment. Currently, the security system for office space that is integrated with digital data has been somewhat limited. The main problem is that office space security items are not guaranteed as there might be outsiders who can enter the office. Therefore, this study aims to develop a system using biometric (face) recognition based on Viola-Jones and Radial Basis Function Network (RBFN) algorithm to ensure office room security. Based on the results, the system developed shows that object detection can work well with an object detection rate of 80%. This system has a pretty good accuracy because the object matching success is 73% of the object detected. The final result obtained from this study is a prototype development for office security using face recognition features that are useful to improve safety and comfort for occupants of office space (due to the availability of access rights) so that not everyone can enter the office.
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Soni, Laxmi Narayan, and Akhilesh A. Waoo. "A Lightweight and Efficient Hybrid CNN Model for Face Detection." International Journal of Environmental Sciences 11, no. 8s (2025): 583–91. https://doi.org/10.64252/edmkva81.
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Face detection under occlusion remains a significant challenge in real-world computer vision applications. This paper proposes a hybrid two-stage detection framework that integrates the real-time efficiency of the Viola-Jones algorithm with the Accuracy of a lightweight, modified AlexNet-based Convolutional Neural Network (CNN). The system initially uses Viola-Jones to propose candidate face regions, which are then verified by the CNN trained on over 70,000 face and non-face images, half of which include partial occlusions such as masks, sunglasses, or hands. CNN incorporates dropout and batch normalisation to ensure robust generalisation. Experimental results demonstrate that the proposed hybrid model achieves a detection accuracy of 93%, precision of 95%, and a false positive rate of only 3%, outperforming state-of-the-art models such as MTCNN, SSD, YOLOv3, and RetinaFace in occlusion-specific scenarios. With a processing speed of approximately 12 frames per second on standard CPU hardware and a memory footprint of only 60 MB, the model is well-suited for real-time applications like surveillance and access control in occlusion-prone environments.
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Murthy, Chinthakindi Balaram, Mohammad Farukh Hashmi, Neeraj Dhanraj Bokde, and Zong Woo Geem. "Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review." Applied Sciences 10, no. 9 (2020): 3280. http://dx.doi.org/10.3390/app10093280.
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In recent years there has been remarkable progress in one computer vision application area: object detection. One of the most challenging and fundamental problems in object detection is locating a specific object from the multiple objects present in a scene. Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks. From 2012 onward, deep learning-based techniques were used for feature extraction, and that led to remarkable breakthroughs in this area. This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques. Several topics have been included, such as Viola–Jones (VJ), histogram of oriented gradient (HOG), one-shot and two-shot detectors, benchmark datasets, evaluation metrics, speed-up techniques, and current state-of-art object detectors. Detailed discussions on some important applications in object detection areas, including pedestrian detection, crowd detection, and real-time object detection on Gpu-based embedded systems have been presented. At last, we conclude by identifying promising future directions.
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A, Pavan Narayana, Janardhan Guptha S, Deepak S, and Pujith Sai P. "Smart Door / COVID-19 Face Mask Detection." International Journal of Innovative Technology and Exploring Engineering 10, no. 9 (2021): 87–92. http://dx.doi.org/10.35940/ijitee.i9369.0710921.
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January 27 2020, a day that will be remembered by the Indian people for a few decades, where a deadly virus peeped into a life of a young lady and till now it has been so threatening as it took up the life of 3.26 lakh people just in India. With the start of the virus government has made mandatory to wear masks when we go out in to crowded or public areas such as markets, malls, private gatherings and etc. So, it will be difficult for a person in the entrance to check whether everyone one are entering with a mask, in this paper we have designed a smart door face mask detection to check whether who are wearing or not wearing mask. By using different technologies such as Open CV, MTCNN, CNN, IFTTT, ThingSpeak we have designed this face mask detection. We use python to program the code. MTCNN using Viola- Jones algorithm detects the human faces present in the screen The Viola-Jones algorithm first detects the face on the grayscale image and then finds the location on the colored image. In this algorithm MTCNN first detects the face in grayscale image locates it and then finds this location on colored image. CNN for detecting masks in the human face is constructed using sample datasets and MobileNetV2 which acts as an object detector in our case the object is mask. ThingSpeak is an open-source Internet of things application used to display the information we get form the smart door. This deployed application can also detect when people are moving. So, with this face mask detection, as a part to stop the spread of the virus, we ensure that with this smart door we can prevent the virus from spreading and can regain our happy life.
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Pavan, Narayana A., Guptha S. Janardhan, S. Deepak, and Sai P. Pujith. "Smart Door / COVID-19 Face Mask Detection." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 9 (2021): 87–92. https://doi.org/10.35940/ijitee.I9369.0710921.
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January 27 2020, a day that will be remembered by the Indian people for a few decades, where a deadly virus peeped into a life of a young lady and till now it has been so threatening as it took up the life of 3.26 lakh people just in India. With the start of the virus government has made mandatory to wear masks when we go out in to crowded or public areas such as markets, malls, private gatherings and etc. So, it will be difficult for a person in the entrance to check whether everyone one are entering with a mask, in this paper we have designed a smart door face mask detection to check whether who are wearing or not wearing mask. By using different technologies such as Open CV, MTCNN, CNN, IFTTT, ThingSpeak we have designed this face mask detection. We use python to program the code. MTCNN using Viola- Jones algorithm detects the human faces present in the screen The Viola-Jones algorithm first detects the face on the grayscale image and then finds the location on the colored image. In this algorithm MTCNN first detects the face in grayscale image locates it and then finds this location on colored image. CNN for detecting masks in the human face is constructed using sample datasets and MobileNetV2 which acts as an object detector in our case the object is mask. ThingSpeak is an open-source Internet of things application used to display the information we get form the smart door. This deployed application can also detect when people are moving. So, with this face mask detection, as a part to stop the spread of the virus, we ensure that with this smart door we can prevent the virus from spreading and can regain our happy life.
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Patil, Santosh, N. Ramakrishnaiah, and S. Laxman Kumar. "Enhanced approach for face detection and identifying human body proportionality using v-jones algorithm." International Journal of Engineering & Technology 7, no. 4 (2018): 2374. http://dx.doi.org/10.14419/ijet.v7i4.14734.
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Manual analysis of pedestrians and crowds is often impractical for massive datasets of surveillance videos. Automatic tracking of humans is one of the essential abilities for computerized analysis of such videos. In this proposed work we use Viola jones method for detecting moving human object, next using same method we identify the Human anatomy body proportion to detect the whole human body. The final function is the skin color threshold using the HIS and YCbCr. The proposed method yields high accuracy, we conducted experimental analysis on different videos, achieved high accuracy in detecting human object moment. Several future enhancements can be made to the system. The detection and tracking of multiple people can be extended to real-time live video. Apart from the detection and tracking, process of recognition can also be done.
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Utami, Febiannisa, Suhendri Suhendri, and Muhammad Abdul Mujib. "Implementasi Algoritma Haar Cascade pada Aplikasi Pengenalan Wajah." Journal of Information Technology 3, no. 1 (2021): 33–38. http://dx.doi.org/10.47292/joint.v3i1.45.
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The large number of citizens in an organization makes the development of an attendance system or citizen detection in a place important in the running of work activities in the organization. Utilization of an IP Camera which is only used for regular monitoring without further detection of the needs of citizens in the organization made the development of personnel detection developed for monitoring the presence of personnel. With the development of a face detection system, it is hoped that the facial algorithm development system will be developed using an IP Camera. Face detection has been developed which has many and special features which aim to determine whether or not a face has been detected in an image. With image management that is developed in face detection, detection will be faster and more accurate because the color is processed into gray degrees so that there are fewer color pixels than those with colors. By using the Python programming language and an image detection library called OpenCV, less code will be designed. This study uses the Viola Jones method, which is a fast and accurate face detection method developed by Paul Viola and Michael Jones. In this study, the Viola Jones method uses the Haar Cascade algorithm which functions as a detection feature in the system and is combined with the internal image process and the AdaBoost Learning and Cascade Classifier so that the detected face object will easily classify whether the object is a face or not. In this case the Cascade Classfier used in this study is the face and eyes. The development of this algorithm is carried out for face detection and recognition. The detection is done by taking pictures with the process taken using a webcam. The system will take several pictures and then the image data will be stored in a folder called dataSet. After that, all data is trained so that it can be recognized by the system. With retrieval, detection and recognition limitations that can only be taken from a distance of less than three meters, face detection on the IP Camera can still read objects other than faces. With recognition and accuracy on the webcam camera, about 80,5% this system can be developed with the Haar Cascade algorithm and the Haar Cascade algorithm precisely to be applied to the development of faced detection and face recognition. By developing the Haar Cascade algorithm for face detection, problems and utilization of an organization's data can be more easily detected and used by IP cameras that can support the performance process of face detection and recognition
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Nidom, Mohammad Saichu. "Haar Cascade Classifier and Adaboost Algorithm for Face Detection with the Viola-Jones Method." Transactions on Informatics and Data Science 2, no. 1 (2025): 15–26. https://doi.org/10.24090/tids.v2i1.12276.
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Face detection is a significant challenge in image processing and computer vision, with broad security, identity recognition, and human-computer interaction applications. This study explores the effectiveness of the Haar Cascade Classifier method optimized with Adaboost to improve the accuracy and efficiency of face detection in various head covering conditions. In this experiment, two approaches were compared: using the Haar Cascade Classifier independently and in combination with Adaboost, with evaluation based on metrics such as accuracy, precision, sensitivity, and F1-Score. The results showed that the Adaboost combination significantly improved detection accuracy, with the "Hooded" class achieving an accuracy of 99.2% and the average detection time reduced from 14.9 seconds to 1.9 seconds. These findings show that the use of optimization techniques such as Adaboost not only improves detection performance but also overall system efficiency. The conclusion of this study emphasizes the importance of combining methods in developing a more robust and efficient face detection system. The implications of this research can be applied to create more effective security and facial recognition applications and pave the way for further study in optimizing object detection algorithms.
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Buana, I. Komang Setia. "Penerapan Pengenalan Wajah Untuk Aplikasi Absensi dengan Metode Viola Jones dan Algoritam LBPH." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 3 (2021): 1008. http://dx.doi.org/10.30865/mib.v5i3.3008.
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The human face can be used to assess because of its uniqueness based on certain parameters. To perform facial recognition, the first thing that needs to be done is face detection. The author uses the Viola-Jones method to detect faces. The Viola-Jones method is known to have high speed and accuracy because it combines several concepts (Haar Features, Integral Image, AdaBoost, and Cascade Classifier) into the main method for handling objects. The principle of camera face recognition itself is that the captured face object will be processed and compared with all face images in the existing data set so that the identity of the face is known. One of the applications of face recognition is to do attendance with individual faces. The attendance process does not need physical contact interactions between humans and devices such as the fingerprint system so that during the current COVID-19 pandemic, the spread of the virus can reduce. In this research, a system that can be checked and a person's face is used as a leverage medium for arrival and return attendance using the Viola-Jones method and the LBPH algorithm. The language used is python with the OpenCV library. The PHP language is used for the user interface so that users perform attendance via a browser with the MySQL database to store attendance data. The result of the research is that using the Viola-Jones method and the LBPH algorithm faces are identified and the data is stored in the database used for data attendance. Distance and slope affect the results of face recognition. The distance is too close about 30 cm from the camera, the face cannot be detected. Instead of face position is too far approximately 200 cm, the face can still be detected but could not be identified. For a face tilt level of about 20o from perpendicular, it can still be recognized, but at a tilt degree of about 30o up or to the right, faces cannot be detected.
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Magnuska, Zuzanna Anna, Benjamin Theek, Milita Darguzyte, et al. "Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification." Cancers 14, no. 2 (2022): 277. http://dx.doi.org/10.3390/cancers14020277.
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Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola–Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola–Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.
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Sunita, Yadav*1 &. Ms. Shalini Kashyap2. "REAL TIME HUMAN FACE DETECTION IN SURVILLANCE SECURITY SYSTEM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 580–85. https://doi.org/10.5281/zenodo.1228621.
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Today’s institutions are facing major security issues; consequently, they need several specially trained personnel to attain the desired security. These personnel, as human beings, make mistakes that might affect the level of security. A proposed solution to the aforementioned matter is a Face Recognition Security System, which can detect intruders to restricted or high-security areas, and help in minimizing human error. This system is composed of two parts: hardware part and software part. The hardware part consists of a camera, while the software part consists of face-detection and face-recognition algorithms software. When a person enters to the zone in question, a series of snapshots are taken by the camera and sent to the software to be analyzed and compared with an existing database of trusted people. An alarm goes off if the user is not recognized. This is a project to develop an e-mail application using HCI features helping users check their mailbox easier. It is consist of a network module to develop a standalone email application to ask user home security. a face recognition module Face recognition algorithm and pick the fastest one to avoid lag and use it as a security level of application. This paper describes the technique for real time human face detection and tracking using a modified version of the algorithm suggested by Paul viola and Michael Jones. The paper starts with the introduction to human face detection and tracking, followed by apprehension of the Vila Jones algorithm and then discussing about the implementation in real video applications. Viola Jones algorithm was based on object detection by extracting some specific features from the image. We used the same approach for real time human face detection and tracking. Simulation results of this developed algorithm shows the Real time human face detection and tracking supporting up to 50 human faces. This algorithm computes data and produce results in just a mere fraction of seconds
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KOWSALYA, R., and N. KHADIR KUMAR. "ROAD SAFETY BY REAL TIME EYE DETECTION FOR DRIVER DROWSINESS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25961.
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Automobile has become a major part in our lives. They are used for transportation of people, items and many other from one place to another. Even though the driver is tired, sleepy or has consumed alcohol he/she tries to drive a vehicle without any anxiety. As a result, it causes road accidents, injuries, loss of lives and damage to property. The proposed solution will be implemented using image processing, computer vision and facial recognition techniques to increase the efficiency and the accuracy of the system. To avoid such problems, an Eye-tracing system based on camera is proposed. By using this system, it detects and gives alert to the driver's Distraction or sleepiness. For this the dashboard is chosen as best position for camera without giving difficulties to the driver. The system will detect the driver's face and eyes by using Viola-Jones Algorithm that includes Hear Classifiers that shows advantage in processing accurate time and detection algorithms. A prepared framework is tested in a simulator provides real driving conditions in an indoor environment. The system is added in real-vehicle for testing it in an outdoor environment. If the system detects that the driver is under distraction or sleepiness it gives an alert to the driver by displaying message on a screen and an audible sound for more attention. Key Words: Computer vision and facial recognition techniques, Viola-jones algorithm, Stimulator.
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Usilin, S. A., O. A. Slavin, and V. V. Arlazarov. "Memory Consumption and Computation Efficiency Improvements of Viola–Jones Object Detection Method for Remote Sensing Applications." Pattern Recognition and Image Analysis 31, no. 3 (2021): 571–79. http://dx.doi.org/10.1134/s1054661821030238.
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Santos, David O., Jugurta Montalvão, Charles A. C. Araujo, Ulisses D. E. S. Lebre, Tarso V. Ferreira, and Eduardo O. Freire. "Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection." Sensors 24, no. 13 (2024): 4219. http://dx.doi.org/10.3390/s24134219.
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This paper explores a data augmentation approach for images of rigid bodies, particularly focusing on electrical equipment and analogous industrial objects. By leveraging manufacturer-provided datasheets containing precise equipment dimensions, we employed straightforward algorithms to generate synthetic images, permitting the expansion of the training dataset from a potentially unlimited viewpoint. In scenarios lacking genuine target images, we conducted a case study using two well-known detectors, representing two machine-learning paradigms: the Viola–Jones (VJ) and You Only Look Once (YOLO) detectors, trained exclusively on datasets featuring synthetic images as the positive examples of the target equipment, namely lightning rods and potential transformers. Performances of both detectors were assessed using real images in both visible and infrared spectra. YOLO consistently demonstrates F1 scores below 26% in both spectra, while VJ’s scores lie in the interval from 38% to 61%. This performance discrepancy is discussed in view of paradigms’ strengths and weaknesses, whereas the relatively high scores of at least one detector are taken as empirical evidence in favor of the proposed data augmentation approach.
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Santosh, Mamta, and Avinash Sharma. "A Proposed Framework for Emotion Recognition Using Canberra Distance Classifier." Journal of Computational and Theoretical Nanoscience 16, no. 9 (2019): 3778–82. http://dx.doi.org/10.1166/jctn.2019.8250.
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Facial Expression Recognition has become the preliminary research area due to its importance in human-computer interaction. Facial Expressions conveys the major part of information so it has vast applications in various fields. Many techniques have been developed in the literature but there is still a need to make the current expression recognition methods efficient. This paper represents proposed framework for face detection and recognizing six universal facial expressions such as happy, anger, disgust, fear, surprise, sad along with neutral face. Viola-Jones method and Face Landmark Detection method are used for face detection. Histogram of oriented gradients is used for feature extraction due to its superiority over other methods. To reduce the dimensionality of features Principal Component Analysis is used so that the maximum variation is preserved. Canberra distance classifier is used for classifying the expressions into different emotions. The proposed method is applied on Japanese Female Facial Expression Database and have evaluated that the proposed method outperforms many state-of-the-art techniques.
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Mahammad, Shoaib Mulla, H. Saralaya Sameer, Ziaullah Mohammad, Makandar Arif, and M. Benazir. "Home Security System Using IoT and Facial Emotion Detection & Alleviation Effects." Research and Applications: Emerging Technologies 5, no. 2 (2023): 14–18. https://doi.org/10.5281/zenodo.8210587.
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<em>Everyone has good mood/bad, if her/she hear encouragement statements, may feel better. </em><em>Facial recognition system</em><em> refers to a computer application or technology that can identify or verify individuals based on their facial features from digital images or video frames. The system achieves this by comparing specific facial characteristics with those stored in a face database.</em> <em>Hence</em><em>, a</em><em> system is developed which captures the footages of person and depending on expressions, takes necessary actions. </em><em>The proposed system uses </em><em>Viola-Jones algorithm</em><em>. </em><em>This algorithm has four stages: haar feature selection, creating an integral image, adaboost training and cascading classifiers. Integral images are a technique used in computer vision and object detection to efficiently calculate the sum of pixel intensities within rectangular regions, which helps in identifying specific patterns or objects in an image. These integral image-based features are commonly used in various object detection algorithms like Haar cascades, which are used in face detection, among other applications.</em> <em>After creating integral image by Haar feature and with the help of training an efficient algorithm is applied to train and detect the region of interest of our facial images and provide response to the emotion of the viewer and alleviate the viewer’s emotion by musical therapy and Updating the identified person information to authorized user via Internet of things(IoT) Module.</em>
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James Loresco, Pocholo, Argel Bandala, Alvin Culaba, and Elmer Dadios. "Computer vision performance metrics evaluation of object detection based on Haar-like, HOG and LBP features for scale-invariant lettuce leaf area calculation." International Journal of Engineering & Technology 7, no. 4 (2019): 4866–72. http://dx.doi.org/10.14419/ijet.v7i4.26071.
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Leaf area can be used as a growth parameter as such it increases as the stage of lettuce progresses. Consideration of scale invariance in estimating the area poses challenging machine vision problems in a smart farm setup. To address this, a marker with a known area is utilized for the system for normalizing area measurements. This study proposes an automated object detection (marker) using Viola-Jones algorithm that uses Haar-like, HOG and LBP features. Performances of the system using each feature at varying illuminations and distances are then compared. Based on the result of this study, the highest performance in general, based on accuracy, precision, and false positive rate is achieved using HOG features. Â
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Zakharov, A. A., A. E. Barinov, A. L. Zhiznyakov, and V. S. Titov. "Object detection in images with a structural descriptor based on graphs." Computer Optics 42, no. 2 (2018): 283–90. http://dx.doi.org/10.18287/2412-6179-2018-42-2-283-290.
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We discuss the development of a structural descriptor for object detection in images. The descriptor is based on a graph, whose vertices are the centers of mass of segment features. The embedding of the graph in a vector space is implemented using a Young-Householder decomposition and based on differential geometry. Compound curves are used to describe the relationship between the points. The image graph is described by a matrix of curvature parameters. The distance matrix for the graphs of the candidate object and the reference object is calculated using the Hausdorff metric. A multidimensional scaling method is used to represent the results. Images of test objects and images of human faces are used to study the developed approach. A comparison of the developed descriptor with the Viola-Jones method is performed when detecting a human head in the image. The advantage of the developed approach is the image rotational invariance in the plane while searching for objects. The descriptor can detect objects rotated in space by angles of up to 50 degrees. Using the mass centers of segments of features as the graph vertices makes the approach more robust to changes in image acquisition angles in comparison with the approach that uses image features as the graph vertices.
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Simra, Kamil*. "FACE DETECTION AND TRACKING SURVEILLANCE SECURITY SYSTEMS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 211–16. https://doi.org/10.5281/zenodo.1213062.
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In this paper, we propose a framework that takes the participation of students for classroom lecture. The proposed system framework takes the participation naturally utilizing face identification and recognition. This participation is recorded by utilizing a camera connected as a part of front of banks, homes, research areas, The basic problem to be solved is to implement an algorithm for detection of faces in an image. At afirst glance the task of face detection may not seem so overwhelming especially considering how easy it is solved by a human. However there is a stark contrast to how difficult it actually is to make a computer successfully solve this task. In order to ease the task Viola-Jones limit themselves to full view frontal upright faces. That is, in order to be detected the entire face must point towards the camera and it should not be tilted to anyside. This may compromise the requirement for being unconstrained a little bit, but considering thatthe detection algorithm most often will be succeeded by a recognition algorithm these demands seem quite reasonable.
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Sanjaya, Kadek Oki, Gede Indrawan, and Kadek Yota Ernanda Aryanto. "PENDETEKSIAN OBJEK ROKOK PADA VIDEO BERBASIS PENGOLAHAN CITRA DENGAN MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER." International Journal of Natural Science and Engineering 1, no. 3 (2018): 92. http://dx.doi.org/10.23887/ijnse.v1i3.12938.
Full textAbstract:
Object detection is a topic widely studied by the scientists as a special study in image processing. Although applications of this topic have been implemented, but basically this technology is not yet mature, futher research is needed to developed to obtain the desired result. The aim of the present study is to detect cigarette objects on video by using the Viola Jones method (Haar Cascade Classifier). This method known to have speed and high accuracy because of combining some concept (Haar features, integral image, Adaboost, and Cascade Classifier) to be a main method to detect objects. In this research, detection testing of cigarettes object is in samples of video with the resolution 160x120 pixels, 320x240 pixels, 640x480 pixels under condition of on 1 cigarette object and condition 2 cigarettes object. The result of this research indicated that percentage of average accuracy highest 93.3% at condition 1 cigarette object and 86,7% in the condition 2 cigarette object that was detected on the video with resolution 640x480 pixels, while the percentage of accuracy lowest 90% at condition 1cigarette object, and 81,7% at the condition 2 cigarette objects, detected on the video with the lowest resolution 160x120 pixels. The percentage of average errors at detection cigarettes object was inversely with percentage of accuracy. So that the detection system is able to better recognize the object of the cigarette, then the number of samples in the database needs to be improved and able to represent various types of cigarettes under various conditions and can be added new parameters related to cigarette object
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WANG, HAIJING, PEIHUA LI, and TIANWEN ZHANG. "BOOSTED GAUSSIAN CLASSIFIER WITH INTEGRAL HISTOGRAM FOR FACE DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 07 (2007): 1127–39. http://dx.doi.org/10.1142/s0218001407005880.
Full textAbstract:
Novel features and weak classifiers are proposed for face detection within the AdaBoost learning framework. Features are histograms computed from a set of spatial templates in filtered images. The filter banks consist of Intensity, Laplacian of Gaussian (Difference of Gaussians), and Gabor filters, aiming to capture spatial and frequency properties of faces at different scales and orientations. Features selected by AdaBoost learning, each of which corresponds to a histogram with a pair of filter and template, can thus be interpreted as boosted marginal distributions of faces. We fit the Gaussian distribution of each histogram feature only for positives (faces) in the sample set as the weak classifier. The results of the experiment demonstrate that classifiers with corresponding features are more powerful in describing the face pattern than haar-like rectangle features introduced by Viola and Jones.