Journal articles: 'Viola-Jones algorithm'

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Stevanović, Dušan. "OBJECT DETECTION USING VIOLA-JONES ALGORITHM." Knowledge International Journal 28, no. 4 (December 10, 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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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 (May 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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Dherya Bengani and Prof. Vasudha Bah. "Face Detection Using Viola Jones Algorithm." November 2020 6, no. 11 (November 23, 2020): 131–34. http://dx.doi.org/10.46501/ijmtst061124.

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Face detection is one of the most widely researched topics in recent times and is at the helm of the computer vision technology. This paper aims to review and study in detail the implementation of Viola Jones algorithm to detect faces in Realtime. Viola Jones algorithm is reviewed first followed by its main steps which include Haar features, integral image and cascading classifiers.

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Крысин, И. А., and К. А. Гаврилов. "Study of neural network approach application in face detection problem." Informacionno-technologicheskij vestnik, no. 4(30) (December 15, 2021): 111–17. http://dx.doi.org/10.21499/2409-1650-30-4-111-117.

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В статье приводится исследование применения сверточных нейронных сетей с разными структурами при решении задачи обнаружения лиц. Приводятся тесты и сравнение эффективности в сравнении с алгоритмом Viola-Jones по оценкам точности и скорости обработки. Проверяется точность при обнаружении лиц с очками. Результаты доказали возможность применения нейросетевого подхода. В статье описываются возможные варианты применения нейронных сетей в гибриде с алгоритмом Viola-Jones для более эффективного решения. The article brings study of convolutional neural network with various structures application in face detection problem. Tests and effectiveness comparison to Viola-Jones algorithm are carried out in terms of accuracy and speed of processing. Accuracy on face detection with glasses is tested. Results proved the ability of neural network approach application. The article describes possible options of using neural networks with Viola-Jones algorithm to improve performance on the solution.

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Mohamed Hatim, Shahirah. "Drowsy Driver Detection Using Viola-Jones Algorithm." Mathematical Sciences and Informatics Journal 2, no. 2 (November 30, 2021): 51–56. http://dx.doi.org/10.24191/mij.v2i2.13926.

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Drowsy driving is one of the factors that lead to road accidents which can cause dead. This is because driver does not able to give fully attention while driving. There are many factors that lead to driver drowsiness such as driving for a long time, do not have enough sleep and shift work. Thus, this research is proposed to develop a system to detect and alert drowsy driver by using Viola-Jones algorithm. Blinking rate is used as the indicator to determine either the driver is in drowsy or awake state. Viola-Jones algorithm is used to detect driver’s face and eyes in real time. Haar cascade classifier for frontal face and glasses eyes are used to train the system to detect driver’s face and eyes. In order to calculate eye blink, Eye Aspect Ratio (EAR) calculation is used to calculate and estimate of the eye-opening state in this system. The results of testing showed that the system with the Viola-Jones algorithm and Haar cascade classifier able to detect eyes blinking rate at the high accuracy percentages.

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Kirana, Chandra, and Burham Isnanto. "Face Identification For Presence Applications Using Violajones and Eigenface Algorithm." Jurnal Sisfokom (Sistem Informasi dan Komputer) 5, no. 2 (October 1, 2016): 7. http://dx.doi.org/10.32736/sisfokom.v5i2.189.

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Sistem presensi merupakan hal yang sangat penting di dalam suatu lingkup kerja, seperti perkantoran, sekolah, maupun universitas. Sistem presensi saat ini telah berkembang dalam berbagai jenis, diantaranya sistem presensi secara manual, barcode, sidik jari, iris mata dan pengenalan wajah. Saat ini telah rata-rata perusahaan telah menggunakan sistem presensi fingerprint. Sistem presensi menggunakan sidik jari ini masih memiliki kekurangan dan kelemahan yang disebabkan human error, seperti scansidik jari sulit diterima. Hal ini bisa dikarenakan kondisi jari yang tidak normal, seperti basah, kotor, terlalu kering, maupun ujung jari terkelupas. Dengan adanya perkembangan teknologi mobile saat ini, maka diusulkan sebuah sistem presensi berbasis wajah yang akan dibangun dengan menggunakan metode Viola–Jones dan Eigenface pada smartphone. Metode Viola - Jones merupakan algoritma yang paling banyak digunakan untuk mendeteksi wajah dikarenakan metode Viola-Jones mampu mendeteksi secara real-time, cepat, efisien, dan juga mempunyai keakuratan yang sangat tinggi dalam mendeteksi wajah. Metode Viola-Jones ini menggabungkan empat kunci utama yaitu: Haar Like Feature, Integral Image, AdaBoost Learning ,dan Cascade Classifier. Algoritma Eigenface digunakan untuk melakukan identifikasi citra wajah yang terdeteksi dari suatu citra wajah dengan menggunakan Principal Component Analysis (PCA). Setelah dilakukan pengujian, aplikasi presensi wajah yang diusulkan mampu menghasilkan tingkat akurasi sebesar 90,90%.

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AZMI, FADHILLAH, Amir Saleh, and N. P. Dharshinni. "Face Identification on Login Security Using Algorithm Combination of Viola-Jones and Cosine Similarity." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 4, no. 1 (July 20, 2020): 203–11. http://dx.doi.org/10.31289/jite.v4i1.3885.

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Data security by using an alphanumeric combination password is no longer used, so it needs to be added security that is difficult to be manipulated by certain people. One type of security is the type of biometrics technology using face recognition which has different characteristics by combining the Viola-Jones algorithm to detect facial features, GLCM (Gray Level Co-occurrence Matrix) for extracting the texture characteristics of an image, and Cosine Similarity for the measurement of the proximity of the data (image matching). The image will be detected using the Viola-Jones algorithm to get face, eyes, nose, and mouth. The image detection results will be calculated the value of the texture characteristics with the GLCM (Gray Level Cooccurrence Matrix) algorithm. Image matching using cosine similarity will determine or match the data stored in the database with new image input until identification results are obtained. The results obtained in this study get the level of accuracy of the identification of the three algorithms by 77.20% with the amount of data that was correctly identified as many as 386 out of 500 images.Keywords: Security, face recognition, Viola-Jones, Cosine Similarity.

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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 (December 1, 2020): 1513. http://dx.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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I., Ahmed, and Yasser A. "Face Gestures Detection using Improved Viola-Jones Algorithm." International Journal of Computer Applications 182, no. 47 (April 11, 2019): 38–41. http://dx.doi.org/10.5120/ijca2019918719.

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Nandi, Amit Kumar, Palash Poddar, Randhir Kumar, and Sameer Kumar Devarakonda. "A REAL-TIME AUTONOMOUS FACE-TRACKING SYSTEM BASED ON A 2-DOF ARTICULATED MANIPULATOR PLATFORM USING EXTENDED KALMAN FILTER." Acta Mechanica Malaysia 4, no. 2 (September 9, 2021): 40–43. http://dx.doi.org/10.26480/amm.02.2021.40.43.

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There is an increasing demand for autonomous tracking applications in the industrial context which ranges from driver monitoring in semi-autonomous vehicles to human-robot interaction (HRI) to facilitate situational awareness in collaborative robots. In order to address the same, a system to track the human face in real-time has been developed and the system is capable of moves accordingly so that the face always remains in the range of visibility of the autonomous system. The system consists of open-source hardware and software to design the Tracking Algorithms which utilizes the Extended Kalman Filters (EKF) at its core. In addition to the basic model, this paper uses a hybrid model, implemented using both Extended Kalman Filters and Viola Jones in conjunction with Iterative Learning Control (ILC) intelligent tuning of PID loop. Performance evaluation of the system has been done in Solidworks and MATLAB. The proposed model with two different control methodologies along with the modified Extended Kalman Filter and Viola Jones Based algorithm has a shorter delay time and produced stable responses over traditional viola jones wavelets based approach.

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Raghuveera, T., S. Vidhushini, and M. Swathi. "Comparative Study of CAMSHIFT and RANSAC Methods for Face and Eye Tracking in Real-Time Video." International Journal of Intelligent Information Technologies 13, no. 2 (April 2017): 63–75. http://dx.doi.org/10.4018/ijiit.2017040104.

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Real-Time Facial and eye tracking is critical in applications like military surveillance, pervasive computing, Human Computer Interaction etc. In this work, face and eye tracking are implemented by using two well-known methods, CAMSHIFT and RANSAC. In our first approach, a frontal face detector is run on each frame of the video and the Viola-Jones face detector is used to detect the faces. CAMSHIFT Algorithm is used in the real- time tracking along with Haar-Like features that are used to localize and track eyes. In our second approach, the face is detected using Viola-Jones, whereas RANSAC is used to match the content of the subsequent frames. Adaptive Bilinear Filter is used to enhance quality of the input video. Then, we run the Viola-Jones face detector on each frame and apply both the algorithms. Finally, we use Kalman filter upon CAMSHIFT and RANSAC and compare with the preceding experiments. The comparisons are made for different real-time videos under heterogeneous environments through proposed performance measures, to identify the best-suited method for a given scenario.

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Ong, J. W., W. J. Chew, and S. K. Phang. "The application of image processing for monitoring student’s attention level during online class." Journal of Physics: Conference Series 2120, no. 1 (December 1, 2021): 012028. http://dx.doi.org/10.1088/1742-6596/2120/1/012028.

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Abstract With the COVID-19 pandemic still causing the world to be quarantined in their house to prevent the spread of the virus, this means online classes are still the main method of conducting classes. This project aims to help lecturers monitor the students during class as they are having problems checking whether the students are paying attention or not. This project uses the student’s facial features to determine their attention level using two different coding algorithm Viola-Jones and Sobel edge. These two algorithms help to determine what kind of facial expression that the students are making. The Viola-Jones algorithm detects and captures the student’s facial features such as eyes and mouth while the Sobel edge algorithm detects the edges of the facial features to determine whether the eyes and mouth are open or closed. With the data collected it will run through the database to determine the student’s attention level and inform the lecturer.

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Martinez, Pablo, and Martin Barczyk. "Implementation and optimization of the cascade classifier algorithm for UAV detection and tracking." Journal of Unmanned Vehicle Systems 7, no. 4 (December 1, 2019): 296–311. http://dx.doi.org/10.1139/juvs-2018-0033.

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A number of vision-based algorithms designed to detect and track unmanned aerial vehicles (UAVs) from on board a second UAV have been researched, implemented, and experimentally validated over the last decade. However, the successful methods have tended to rely on characteristics such as color or shape, meaning they require the target UAV to have particular markings or geometries. This paper uses the Viola–Jones cascade classifier, a computer vision algorithm originally designed to detect human faces in video streams, and demonstrates its capability for detecting and tracking an arbitrary type of UAV with excellent performance in either indoor or outdoor environments and with a variety of backgrounds. The Viola–Jones algorithm is applied to two specific quadrotor UAV models, the Solo from 3D Robotics and the AR.Drone 2.0 from Parrot. Experimental testing demonstrates that the resulting system achieves very good detection and tracking performance in real time on each UAV type for both indoor and outdoor flight tests.

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Putta, Rohan, Gayatri N Shinde, and Punit Lohani. "Real Time Drowsiness Detection System using Viola Jones Algorithm." International Journal of Computer Applications 95, no. 8 (June 18, 2014): 28–34. http://dx.doi.org/10.5120/16615-6459.

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Wang, Yi-Qing. "An Analysis of the Viola-Jones Face Detection Algorithm." Image Processing On Line 4 (June 26, 2014): 128–48. http://dx.doi.org/10.5201/ipol.2014.104.

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Egorov, A. D., A. N. Shtanko, and P. E. Minin. "Selection of Viola–Jones algorithm parameters for specific conditions." Bulletin of the Lebedev Physics Institute 42, no. 8 (August 2015): 244–48. http://dx.doi.org/10.3103/s1068335615080060.

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Khryashchev, V. V., A. A. Lebedev, and A. L. Priorov. "ENHANCEMENT OF FAST FACE DETECTION ALGORITHM BASED ON A CASCADE OF DECISION TREES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W4 (May 10, 2017): 237–41. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w4-237-2017.

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Face detection algorithm based on a cascade of ensembles of decision trees (CEDT) is presented. The new approach allows detecting faces other than the front position through the use of multiple classifiers. Each classifier is trained for a specific range of angles of the rotation head. The results showed a high rate of productivity for CEDT on images with standard size. The algorithm increases the area under the ROC-curve of 13% compared to a standard Viola-Jones face detection algorithm. Final realization of given algorithm consist of 5 different cascades for frontal/non-frontal faces. One more thing which we take from the simulation results is a low computational complexity of CEDT algorithm in comparison with standard Viola-Jones approach. This could prove important in the embedded system and mobile device industries because it can reduce the cost of hardware and make battery life longer.

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Kulkarni, Narayan, and Ashok V. Sutagundar. "Detection of Human Facial Parts Using Viola-Jones Algorithm in Group of Faces." International Journal of Applied Evolutionary Computation 10, no. 1 (January 2019): 39–48. http://dx.doi.org/10.4018/ijaec.2019010103.

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Face detection is an image processing technique used in computer system to detect face in digital image. This article proposes an approach to detect faces and facial parts from an image of a group of people using the Viola Jones algorithm. Face detection is used in face recognition and identification systems. Automatic face detection and recognition is most challenging and a fast-growing research area in real-time applications like CC TV surveillance, video tracking, facial expression recognition, gesture recognition, human computer interaction, computer vision, and gender recognition. For face detection purposes various techniques and methods are applied in a computer system. In proposed system, a Viola Jones algorithm is implemented for multiple faces and facial parts and detected with a high rate of accuracy.

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Ibrahim, S., K. R. Jamaluddin, and K. A. F. A. Samah. "Security Authentication for Student Cards’ Biometric Recognition Using Viola-Jones Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 1 (July 1, 2018): 241. http://dx.doi.org/10.11591/ijeecs.v11.i1.pp241-247.

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The unauthorized access to the university entrance could be gained by only flashing a student card. This unsecure situation shows the loophole of security authentication in a university. In order to overcome this, a biometric recognition could be the most suitable candidate as it varies uniquely from one person to another. A study on student cards’ biometric recognition using Viola-Jones algorithm is presented as it is proven as a powerful algorithm in terms of superb detection rates and speed. It is done by comparing the facial structures and features between the student card’s image and the card holder image, thus determining the similarity. The recognition performance is evaluated based on the percentage of similarity using 100 testing images of 50 students. The observation on results obtained the effectiveness of the Viola-Jones features in student cards’ biometric recognition rate.

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Ahmad, Samsiah, Muhammad Farhan Ramli, Zalikha Zulkifli, and Lily Marlia Abdul Latif. "Wireless IoT Smart Door Lock Using Viola-Jones Face Detection Technique." Mathematical Sciences and Informatics Journal 1, no. 2 (November 30, 2020): 70–76. http://dx.doi.org/10.24191/mij.v1i2.14195.

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Technology in Industrial revolution 4.0 has rapidly changed with the advancement technology developments, including the technology of Internet of Things (IoT). With IoT, different types of data either structured or unstructured can be collected and transferred over the Internet that attracted researchers to conduct various empirical studies on automation of home security environment mainly with intelligence system. This project highlights a face detection of smart door lock system based on Viola-Jones technique. The fundamental system design, implementation of hardware and software as well as the data collection and processing techniques are described in this paper. The prototype of the wireless IoT Smart Door Lock based on Viola-Jones face detection technique has been tested and the accuracy of classification at different face angles (front, left, right, top, down) were recorded and also presented in this paper. The results show that Viola- Jones algorithm has achieved 88% of average accuracy on the complete faces classification.

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Pratama, Irham Surya, and Felix Indra Kurniadi. "Ethnicity Classification Based On Facial Features Using Viola-Jones Algorithm." IJNMT (International Journal of New Media Technology) 7, no. 1 (July 2, 2020): 39–42. http://dx.doi.org/10.31937/ijnmt.v6i2.917.

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Biometric recognition system can use race classification to identify the human globally with a particular identity. This paper proposes Support Vector Machine and will compare the result with K-Nearest Neighbor for classification of people into two major races namely Indonesian western and eastern races. Firstly, the proposed classification method extracts the distinct primary facial feature and skin color model of the given face with Viola-Jones Algorithm to effectively classify the races. To increase the accuracy, the sample must not contain any background of other people skin, no movement and the pictures were taken from the mobile camera with no beauty filter.

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Wang, Linlin, Wenwei Xiao, Yuan Qi, Qichao Gao, Lin Li, Kangting Yan, Yali Zhang, and Yubin Lan. "Farmland human-shape obstacles identification based on Viola-Jones Algorithm." International Journal of Precision Agricultural Aviation 1, no. 1 (2018): 35–40. http://dx.doi.org/10.33440/j.ijpaa.20200303.99.

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Naik, Ajit, Divyesh Darde, Shraddha Chitalia, and Vidya Zope. "Crowd Management using Viola Jones Algorithm and Heuristic Data Mining." International Journal of Computer Applications 84, no. 17 (December 18, 2013): 9–13. http://dx.doi.org/10.5120/14676-2949.

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Hamdan, Mohammad, and Hisham A. Shehadeh. "Automated Quantification of Eye Blink Rate Using VIOLA–JONES Algorithm." International Journal of Technology Diffusion 9, no. 4 (October 2018): 19–32. http://dx.doi.org/10.4018/ijtd.2018100102.

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In this article, we have proposed a novel tool that helps to objectively quantify eye blink rate. Using the proposed algorithm, a threshold for normal blink rate can be set to test those who have to reduce eye blink rate and are prone to ocular surface dryness. The statistical results show excellent agreement between software-detected number of blinks and visually measured with 90% accuracy for the participants. In addition, the comparison between our tool and other approaches of eye blink monitoring shows that our tool is competitive with only 5% wasted blinks.

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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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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 (June 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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Aashish, Kamath, and A. Vijayalakshmi. "Comparison of Viola-Jones And Kanade-Lucas-Tomasi Face Detection Algorithms." Oriental journal of computer science and technology 10, no. 1 (March 9, 2017): 151–59. http://dx.doi.org/10.13005/ojcst/10.01.20.

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Face detection technologies are used in a large variety of applications like advertising, entertainment, video coding, digital cameras, CCTV surveillance and even in military use. It is especially crucial in face recognition systems. You can’t recognise faces that you can’t detect, right? But a single face detection algorithm won’t work in the same way in every situation. It all comes down to how the algorithm works. For example, the Kanade-Lucas-Tomasi algorithm makes use of spatial common intensity transformation to direct the deep search for the position that shows the best match. It is much faster than other traditional techniques for checking far fewer potential matches between pictures. Similarly, another common face detection algorithm is the Viola-Jones algorithm that is the most widely used face detection algorithm. It is used in most digital cameras and mobile phones to detect faces. It uses cascades to detect edges like the nose, the ears etc. However, if there is a group of people and their faces are close to each other, the algorithm might not work that well as edges tend to overlap in a crowd. It might not detect individual faces. Therefore, in this work, we test both the Viola-Jones and the Kanade-Lucas-Tomasi algorithm for each image to find out which algorithm works best in which scenario.

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PNithish Sriman, K., P. Raj Kumar, A. Naveen, and R. Saravana Kumar. "Comparison of Paul Viola – Michael Jones algorithm and HOG algorithm for Face Detection." IOP Conference Series: Materials Science and Engineering 1084, no. 1 (March 1, 2021): 012014. http://dx.doi.org/10.1088/1757-899x/1084/1/012014.

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Bendjillali, Ridha, Mohammed Beladgham, Khaled Merit, and Abdelmalik Taleb-Ahmed. "Improved Facial Expression Recognition Based on DWT Feature for Deep CNN." Electronics 8, no. 3 (March 15, 2019): 324. http://dx.doi.org/10.3390/electronics8030324.

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Facial expression recognition (FER) has become one of the most important fields of research in pattern recognition. In this paper, we propose a method for the identification of facial expressions of people through their emotions. Being robust against illumination changes, this method combines four steps: Viola–Jones face detection algorithm, facial image enhancement using contrast limited adaptive histogram equalization (CLAHE) algorithm, the discrete wavelet transform (DWT), and deep convolutional neural network (CNN). We have used Viola–Jones to locate the face and facial parts; the facial image is enhanced using CLAHE; then facial features extraction is done using DWT; and finally, the extracted features are used directly to train the CNN network, for the purpose of classifying the facial expressions. Our experimental work was performed on the CK+ database and JAFFE face database. The results obtained using this network were 96.46% and 98.43%, respectively.

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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 (July 31, 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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Lakshmi, N. Dhana, Pranavi Nagubandi, Muralidhar Yelet, and K. Vishnu Vardan. "Automated Attendance System Based on Facial Recognition using Viola-Jones Algorithm." Asian Journal of Applied Science and Technology 06, no. 02 (2022): 82–91. http://dx.doi.org/10.38177/ajast.2022.6210.

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Facial Recognition is the most used technology nowadays. Apart from Bio-metrics, Iris scan, fingerprint recognition methodologies, facial recognition is emerging recognition methodology these days. One of the most effective applications of this methodology is automated attendance using facial recognition, which is contact less, secure, and effective unlike in tradition way (manual attendance) it saves more time. Methodology used in this project involves Viola-Jones algorithm for face detection and Eigenfaces approach for feature selection and classification. In Viola-jones algorithm inputs are taken as captured images of individual persons and produce a dataset containing cropped images of individual and these dataset is directed to Eigenfaces approach as input and training of data occurs through the process of calculating eigen vectors for each eigenface. At the time of testing, Euclidean distance between eigen vectors of testing image and eigen vectors of trained eigen faces determines the matched individual. Facial recognition can also be done with PCA, which has 79.6 percent accuracy, and LBPH, which has 90.23 percent accuracy. However, when employing the Eigenfaces technique, the accuracy is 93.07 percent. MATLAB software with Computer Vision Toolbox and Deep Learning Toolbox is used for this work.

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B, Ramakrishna, and M. Sharmila Kumari. "Implementation of combined Viola-Jones and NPD Based Face Detection Algorithm." International Journal of Computer Sciences and Engineering 6, no. 6 (June 30, 2018): 1518–22. http://dx.doi.org/10.26438/ijcse/v6i6.15181522.

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Yaakub, Nur Dini Ayuni, Mohd Wafi Nasrudin, Vijayasarveswari Veeraperumal, Iszaidy Ismail, Rashidah Che Yob, Leow Wai Zhe, and Wan Azani Mustafa. "Class Attendance System Using Viola-Jones Algorithm and Principal Component Analysis." Journal of Physics: Conference Series 1962, no. 1 (July 1, 2021): 012053. http://dx.doi.org/10.1088/1742-6596/1962/1/012053.

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Đorić, Dušan, Saša Crnobrnja, Marija Punt, and Mile Davidović. "Real-time face tracking in video content using Viola-Jones algorithm." Telfor Journal 11, no. 1 (2019): 70–75. http://dx.doi.org/10.5937/telfor1901070q.

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Viriri, Serestina, and Brett Lagerwall. "Increasing Face Recognition Rates Using Novel Classification Algorithms." International Journal of Computers Communications & Control 11, no. 3 (March 24, 2016): 381. http://dx.doi.org/10.15837/ijccc.2016.3.571.

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This paper describes and discusses a set of algorithms which can improve ace recognition rates. These algorithms include adaptive K-Nearest Neighbour, daptive weighted average, reverse weighted average and exponential weighted average. ssentially, the algorithms are extensions to the basic classification algorithm sed in most face recognition research. Whereas the basic classification algorithm elects the subject with the shortest associated distance, the algorithms presented in his paper manipulate and extract information from the set of distances between a est image and the training image set in order to obtain more accurate classifications. he base system to which the algorithms are applied uses the eigenfaces technique or recognition with an adapted Viola and Jones algorithm for face extraction. Most f the algorithms proposed show a consistent improvement over the baseline test.

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Ramana, Lovedeep, Wooram Choi, and Young-Jin Cha. "Fully automated vision-based loosened bolt detection using the Viola–Jones algorithm." Structural Health Monitoring 18, no. 2 (February 18, 2018): 422–34. http://dx.doi.org/10.1177/1475921718757459.

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Many damage detection methods that use data obtained from contact sensors physically attached to structures have been developed. However, damage-sensitive features such as the modal properties of steel and reinforced concrete are sensitive to environmental conditions such as temperature and humidity. These uncertainties are difficult to address with a regression model or any other temperature compensation method, and these uncertainties are the primary causes of false alarms. A vision-based remote sensing system can be an option for addressing some of the challenges inherent in traditional sensing systems because it provides information about structural conditions. Using bolted connections is a common engineering practice, but very few vision-based techniques have been developed for loosened bolt detection. Thus, this article proposes a fully automated vision-based method for detecting loosened civil structural bolts using the Viola–Jones algorithm and support vector machines. Images of bolt connections for training were taken with a smartphone camera. The Viola–Jones algorithm was trained on two datasets of images with and without bolts to localize all the bolts in the images. The localized bolts were automatically cropped and binarized to calculate the bolt head dimensions and the exposed shank length. The calculated features were fed into a support vector machine to generate a decision boundary separating loosened and tight bolts. We tested our method on images taken with a digital single-lens reflex camera.

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Gîlcă, Gheorghe, and Nicu-George Bîzdoacă. "A Fuzzy Aproach For Facial Emotion Recognition." ACTA Universitatis Cibiniensis 67, no. 1 (September 1, 2015): 195–200. http://dx.doi.org/10.1515/aucts-2015-0089.

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Abstract This article deals with an emotion recognition system based on the fuzzy sets. Human faces are detected in images with the Viola - Jones algorithm and for its tracking in video sequences we used the Camshift algorithm. The detected human faces are transferred to the decisional fuzzy system, which is based on the variable fuzzyfication measurements of the face: eyebrow, eyelid and mouth. The system can easily determine the emotional state of a person.

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Joshi, Aparna, Vinay Chavan, and Parag Kaveri. "Semantic Gap Reduction From Mouth Feature Threshold Value Using Viola Jones Algorithm." IOP Conference Series: Materials Science and Engineering 1022 (January 19, 2021): 012065. http://dx.doi.org/10.1088/1757-899x/1022/1/012065.

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Lorena Br Ginting, Selvia, Hanhan Maulana, Riffa Alfaridzi Priatna, Deran Deriyana Fauzzan, and Devidli Setiawan. "Crowd Detection Using YOLOv3-Tiny Method and Viola-Jones Algorithm at Mall." International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) 2, no. 2 (2021): 13–22. http://dx.doi.org/10.34010/injiiscom.v2i2.5460.

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Indonesia is one of the countries affected by Covid-19 which is spreading quite fast. Lately, the surge in Covid19 cases in Indonesia is quite high, due to the lack of public awareness of the current health protocols, such as avoiding crowds and keeping a distance. The purpose of this study is to reduce crowds that occur in places with a high risk of crowding, for example in mall. Detection is done by using Closed Circuit Television (CCTV) in the mall and using the YOLOv3-Tiny method and the ViolaJones Algorithm to detect the crowd. To support the research, we use the method of literature study and field observation at Cimahi Mall as one of the malls in the area of Bandung Raya. The results show that to reduce the number of crowds that occur in the mall, crowd detection must be carried out using the YOLOv3-Tiny method and the Viola-Jones Algorithm, and a warning system is given if a crowd is detected in the place. The main concept of this system is crowd detection and warning if there is a crowd located on CCTV in the Mall. In our opinion, when this system is running in malls that occur in Indonesia, the number of positive cases of COVID-19 in Indonesia will decrease because there are no crowds. It can be concluded that this system exists as a precaution against the crowds that often occur today at the mall. Prevention is done by detecting crowds and giving warnings if there is a crowd so that positive cases of COVID-19 in Indonesia will be reduced.

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Nilatika, Aminurachma Aisyah, Khoerul Anwar, and Eka Yuniar. "Masked Face Detection Automation System Using Mask Threshold and Viola Jones Method." Jurnal Riset Informatika 5, no. 1 (December 14, 2022): 521–28. http://dx.doi.org/10.34288/jri.v5i1.470.

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Reducing or even breaking the chain of Covid-19 virus infections during a pandemic is important. The techniques that are encouraged are mandatory hand washing, social distancing, and mandatory wearing of masks. Wearing masks is urgent, therefore requiring people to wear masks is the right policy. This study aims to detect people who use masks or do not use masks by applying the Viola Jones method. In this study, modification of the tresholder algorithm was carried out by applying a mask thresholder for optimization of facial segmentation. Meanwhile viola jones was built by combining several concepts of Haar Feature, Integral Image, AdaBoost, classivier Cascade into a main method for detecting objects. The performance of the proposed method for face detection has an accuracy of 95%, a precision of 94.73%, and a recall of 100%. 5. The masked face detection test has an accuracy of 94%, precision 100%, and recall 90.90%

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Klimov, I. Z., and R. Bustami. "Research and Development of Encryption Algorithm in the Selected Image Area with Viola - Jones Algorithm." Bulletin of Kalashnikov ISTU 19, no. 4 (January 16, 2017): 75. http://dx.doi.org/10.22213/2413-1172-2016-4-75-76.

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Изложены результаты разработки программных средств, реализующих технику обрезки, перемещения, шифрования и дешифрирования выбранного участка изображения. В симуляции использован алгоритм Виолы и Джонса для распознавания лиц. Выполнено сравнение следующих алгоритмов шифрования для решения поставленной задачи: DES и ГОСТ 28147-89, а также алгоритм анализа хаоса. Предлагаемый метод моделируется в графическом пользовательском интерфейсе программирования Matlab.

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Alshifa, S. "Face Mask and Social Distancing Detection Using ML Technique." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3218–22. http://dx.doi.org/10.22214/ijraset.2021.37021.

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Detecting Mask and Social Distance is our main motive in this project.Face detection plays important roles in detecting face mask. Face detection means detecting or searching for a face in an image or video. For face and mask detection we use viola jones algorithm or Haar cascade algorithm using Open CV. For social distancing we use YOLO algorithm. We have created a system which detect the face and then, it will detect nose and mouth to confirm that the person wear mask or not.

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Liu, Yishu, and Jun Li. "Brand Marketing Decision Support System Based on Computer Vision and Parallel Computing." Wireless Communications and Mobile Computing 2022 (March 30, 2022): 1–14. http://dx.doi.org/10.1155/2022/7416106.

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With the rapid development of information technology, decision support systems that can assist business managers in making scientific decisions have become the focus of research. At present, there are not many related studies, but from the brand marketing level, there are not many studies combining smart technology. Based on computer vision technology and parallel computing algorithms, this paper launches an in-depth study of brand marketing decision support systems. First, use computer vision technology and Viola-Jones face detection framework to detect consumers’ faces, and use the classic convolutional neural network model AlexNet for gender judgment and age prediction to analyze consumer groups. Then, use parallel computing to optimize the genetic algorithm to improve the running speed of the algorithm. Design the brand marketing decision support system based on the above technology and algorithm, analyze the relevant data of the L brand, and divide the functional structure of the system into three parts: customer market analysis, performance evaluation, and demand forecasting. The ROC curve of the Viola-Jones face detection framework shows its superior performance. After 500 iterations of the AlexNet model, the verification set loss of the network is stable at 1.8, and the accuracy of the verification set is stable at 38%. Parallel genetic algorithms run 1.8 times faster than serial genetic algorithms at the lowest and 9 times faster at the highest. The minimum prediction error is 0.17%, and the maximum is 2%, which shows that the system can make accurate predictions based on previous years’ data. Computer vision is a technique that converts still image or video data into a decision or a new representation. All such transformations are done to accomplish a specific purpose. Therefore, a brand marketing decision support system based on computer vision and parallel computing can help managers make scientific decisions, save production costs, reduce inventory pressure, and enhance the brand’s competitive advantage.

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Cao, Ke Ming, Yan Long Li, Jian Sheng Chen, Chang Yang, Meng Wang, Huan Shao, and Guang Da Su. "A Fast Implementation of DPM-Based Facial Landmark Localization." Applied Mechanics and Materials 462-463 (November 2013): 416–20. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.416.

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Face detection, pose estimation and facial landmark localization are three fundamental problems in pattern recognition. These three tasks have high request of algorithm efficiency and accuracy. Zhu and Ramanan proposed a model based on mixture of tree structures to solve the three tasks simultaneously and it obtains state-of-the-art result. However, the efficiency of their algorithm is relatively low. Our improved algorithm combines Viola Jones detector and tree-structured model and achieves a speed-up of tens of times even hundreds of times of original algorithm on ordinary laptop according to images of different sizes.

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Usilin, Sergey, and Oleg Slavin. "Using of Viola and Jones Method to Localize Objects in Multispectral Aerospace Images based on Multichannel Features." E3S Web of Conferences 209 (2020): 03027. http://dx.doi.org/10.1051/e3sconf/202020903027.

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A new algorithm for localizing engineering objects on multispectral images based on the Viola and Jones method has been developed. The proposed algorithm uses multichannel features allowing to construct classifiers that are sensitive to features of joint brightness distribution and the brightness distribution in different channels. The algorithm described in the paper provides a precision value of 0.96 and a recall value of 0.99 in the problem of localizing oil storage tank images in a set of aerospace images. The proposed algorithm can be used for visual analytics and automatic detection of various critical objects in aerospace images.

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Senthil, K. Manoj, P. Nirmala Devi, K. G. Praveen Raj, and M. Kavin. "Food management and surveillance using Viola Jones algorithm and Local Binary Pattern Histogram." IOP Conference Series: Materials Science and Engineering 1055, no. 1 (February 1, 2021): 012079. http://dx.doi.org/10.1088/1757-899x/1055/1/012079.

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ANKITA, THAKUR, and RATHORE SAGAR SINGH. "MOUTH AND EYE DETECTION BASED DROWSY DRIVER WARNING SYSTEM USING VIOLA JONES ALGORITHM." i-manager’s Journal on Pattern Recognition 3, no. 3 (2016): 7. http://dx.doi.org/10.26634/jpr.3.3.12405.

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Nair, Aishwarya Radhakrishnan, and Amol D. Potgantwar. "Masked Face Detection using the Viola Jones Algorithm: A Progressive Approach for less Time Consumption." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 6, no. 4 (December 19, 2018): 4. http://dx.doi.org/10.3991/ijes.v6i4.9317.

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The use of CCTV surveillance is today’s need in public and private sector for ensuring security against terrorism and robbery. Regular expressions are used to signify enormous sets of motion attributes captured in video. The video vigilance is popular system without using human interference to capture important scenes. The motive of the work is to introduce automatic revelation of masked objects in real time with a surveillance camera. The main aim is to detect masked person automatically in less time period. In this paper,the researcher proposes a system that consists methods which uses four variant steps that are the steps of calculating distance range of person from the camera, eye or vision line detection and face part detection such as mouth detection and face detection. Performance of proposed algorithm is carried out on various real time inputs. Experimental evaluation shows that proposed algorithm exceeds better in terms of time consumption. This unique approach for the problem has created a method transparent and easier in complexity so that the real time implementation can be made beneficial and workable. Analysis of the algorithms fulfillment on the test video track gives appropriate judgments for additional improvements in the masked face detection performance. Finally, based on the research, the axioms were useful for the work which can be usually accessible from available algorithms.

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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 (March 5, 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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Govindaraj, Ramkumar, and E. Logashanmugam. "Multimodal verge for scale and pose variant real time face tracking and recognition." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 2 (February 1, 2019): 665. http://dx.doi.org/10.11591/ijeecs.v13.i2.pp665-670.

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In recent times face tracking and face recognition have turned out to be increasingly dynamic research field in image processing. This work proposed the framework DEtecting Contiguous Outliers in the LOw-rank Representation for face tracking, in this algorithm the background is assessed by a low-rank network and foreground articles can be distinguished as anomalies. This is suitable for non-rigid foreground motion and moving camera. The face of a foreground person is caught from the frame and then it is contrasted and the speculated pictures stored in the dataset. Here we used Viola-Jones algorithm for face recognition. This approach outperforms the traditional algorithms on multimodal video methodologies and it works adequately on extensive variety of security and surveillance purposes. Results on the continuous demonstrate that the proposed calculation can correctly obtain facial features points. The algorithm is relegate on the continuous camera input and under ongoing ecological conditions.

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Journal articles on the topic 'Viola-Jones algorithm'

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