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Antipona, Clarence Alicante, Romeo Magsino, Raymund Dioses, and Khatalyn Mata. "An Enhancement of Haar Cascade Algorithm Applied to Face Recognition for Gate Pass Security." IC-ITECHS 5, no. 1 (2024): 1–9. https://doi.org/10.32664/ic-itechs.v5i1.1500.
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This study is focused on enhancing the Haar Cascade algorithm to decrease the false positive and false negative rate of face recognition in images with variations in lighting, facial expressions, and occlusions to increase accuracy. The face recognition library was applied with Haar Cascade where 128-dimensional vectors representing the unique features of a face were encoded. The Enhanced Haar Cascade Algorithm produced a 98.39% accuracy rate, in comparison, the Haar Cascade Algorithm achieved a 46.70% - 77.00% accuracy rate. Both algorithms used the Confusion Matrix Test with 301,950 comparisons using the same dataset of 550 images. The 98.39% accuracy rate shows a significant decrease in false positive and false negative rates in facial recognition in images with complex conditions
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Pavani, K. "Novel Vehicle Detection in Real Time Road Traffic Density Using Haar Cascade Comparing with KNN Algorithm based on Accuracy and Time Mean Speed." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 897–910. http://dx.doi.org/10.47059/revistageintec.v11i2.1723.
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Aim: The main objective of the paper is to detect objects in iconic real time traffic density videos from CCTVs and Cameras using Haar Cascade algorithm and to compare algorithms with K-Nearest Neighbour algorithm (KNN). In this case we tried improving the rate of accuracy in predicting the traffic density. Materials and methods: Haar Cascade algorithm is applied on 5 realistic videos and which consists of more than 250 frames. For the same we evaluated the Accuracy and Precision values. Harr-like function displays the vehicle’s visual structure, and the AdaBoost machine learning algorithm was used to create a classifier by combining individual classifiers. The significance value achieved for finding the accuracy and precision was 0.445 and 0.754 respectively. Results and Discussions: Detection of vehicles in high speed videos is performed by using Haar Cascade which has mean accuracy with 85.22% and mean precision with 90.63% and 60% of mean accuracy and 58.53% mean precision in KNN classifiers. Conclusion: The performance of the Haar Cascade appears to be better than KNN in terms of both Accuracy and Precision.
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Erwan, Dede, Yovi Apridiansyah, Erwin Dwika Putra, and Ujang Juhardi. "Algoritma Haar Cascade Deteksi WajahMenggunakan Phyton." JUKOMIKA (Jurnal Ilmu Komputer dan Informatika) 5, no. 2 (2023): 55–65. http://dx.doi.org/10.54650/jukomika.v5i2.461.
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The face is one of the body parts that exist in humans which is often used as a sign of identification between one person and another, so the face can be said to be a unique thing because it has differences. With these differences, faces are often used as a marker of self-identity so that they can be recognized by others. The introduction of this identity is an important thing that is used in various purposes, such as attendance attendance, online exams, banking transactions, online buying and selling, and so on. Even in the development of technology today is increasingly progressing, one of which is facial recognition technology can be used to access security unlocks on smartphones. In this study, face detection can be done using the Haarcascade method. The Haar Cascade algorithm is one of the algorithms used to detect a face. The algorithm is able to detect quickly and in real time an object including a human face. In analyzing face detection using the Haar cascade algorithm, the input image must match what will be detected so that it can detect the face precisely. The light intensity must be sufficient for the face to be detected. The testing process was carried out with a distance of 50 Cm, 80 Cm and 100 Cm with the results of measurements using a successful matrix confussion with a value of 92%.
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Lee, Ching Min, and Yan Ming Li. "Implementation of an Embedded Facial Recognition System." Applied Mechanics and Materials 870 (September 2017): 283–88. http://dx.doi.org/10.4028/www.scientific.net/amm.870.283.
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In this paper, an embedded facial recognition system whose platform consists of pcDuono-V2 board with ARM-processor inside and a Linux-kernel-based operating system, Ubuntu, is implemented. A camera is set up on the platform to take human face images. A facial recognition program consisting of AdaBoost algorithm, Haar-like features, integral image method, and cascade classifiers is utilized to recognize images. The AdaBoost algorithm is a modified Boosting algorithm, which is a machine learning algorithm for training cascade stronger classifiers based on Haar-like features, where Haar-like features are the foundation of the recognition. An integral image method is used to speed up the calculation of corresponding rectangle feature values for Haar-like features. The whole facial recognition comprises facial training procedures and recognition procedures. In facial training procedures, sufficient amounts of positive and negative picture samples are necessary for getting Haar-like features to the recognition system. AdaBoost algorithm is then used to the system for training cascade stronger classifiers which are the detection tools in recognition procedures. While in facial recognition procedures, after getting the Haar-like features for the target images or pictures, cascade stronger classifiers work to detect and recognize. According to the experimental results, the resultant embedded system can recognize the experimental subjects in one second for every our considered situations, which assures the real-time performance.
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Akil, Ibnu. "FACE DETECTION PADA GAMBAR DENGAN MENGGUNAKAN OPENCV HAAR CASCADE." INTI Nusa Mandiri 17, no. 2 (2023): 48–54. http://dx.doi.org/10.33480/inti.v17i2.4000.
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Abstract—OpenCV has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. It has been proven by software companies, that is why the researcher will use it for face detection application with Java programming langguage. The purpose of this paper is trying to implement machine learning library OpenCV with Haarcascade algorithm to detect face from an image and to find the weaknesess of haarcascade algorithm. Haar cascade is proven still relliable to detect face.
 Abstrak— OpenCV memiliki lebih dari 2500 algoritma yang sudah dioptimisasi untuk digunakan dalam computer vision dan pembelajaran mesin. Karena keberhasilannya yang sudah dibuktikan oleh banyak perusahaan perangkat lunak, maka peneliti akan menggunakannya untuk aplikasi face detection dengan menggunakan bahasa pemrograman Java. Tujuan dari artikel ini adalah untuk mencoba menerapkan library pembelajaran mesin OpenCV algoritma Haar cascade untuk mendeteksi wajah pada sebuah gambar dan untuk mencari kelemahannya. Haar cascade telah terbukti masih cukup handal dalam mendeteksi wajah.
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Rastogi, Akanksha, and Beom Sahng Ryuh. "Teat detection algorithm: YOLO vs. Haar-cascade." Journal of Mechanical Science and Technology 33, no. 4 (2019): 1869–74. http://dx.doi.org/10.1007/s12206-019-0339-5.
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S.P., Prof Shinde. "Digital Image Forensics Using Deep Learning and Person Identification." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30421.
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In the realm of computer vision and image processing, the task of digital image identification holds significant importance across various domains. This paper presents a comparative study of two distinct methodologies for digital image identification: deep learning and the Haar cascade algorithm. Deep learning, specifically Convolutional Neural Networks (CNNs), has emerged as a powerful tool for automatically learning hierarchical representations from data and has achieved remarkable success in image-related tasks. In contrast, the Haar cascade algorithm, a classic machine learning technique, offers real-time object detection capabilities with its efficient feature-based approach. Through a series of experiments and evaluations on benchmark datasets, we analyze the performance, strengths, and limitations of these methodologies. Factors such as dataset size, computational resources, and application requirements are considered in the comparison. Our findings provide insights into the suitability of deep learning and the Haar cascade algorithm for various image identification tasks, aiding practitioners and researchers in selecting the most appropriate approach based on specific project needs. This research contributes to advancing the field of image processing and computer vision by offering a comprehensive analysis of these two prominent methodologies in digital image identification. Key Words: Deep Learning, Convolutional Neural Network (CNN), Haar cascade algorithm, image, forgery, detection
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Chau, Khanh Ngan, and Nghi Thanh Doan. "DENSE SIFT FEATURE AND LOCAL NAIVE BAYES NEAREST NEIGHBOR FOR FACE RECOGNITION." Scientific Journal of Tra Vinh University 1, no. 28 (2017): 56–63. http://dx.doi.org/10.35382/18594816.1.28.2017.46.
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Human face recognition is a technology which is widely used in life. There have been much effort on developing face recognition algorithms. In this paper, we present a new methodology that combines Haar Like Features - Cascade of Boosted Classifiers, Dense Scale-Invariant Feature Transform (DSIFT), Local Naive Bayes Nearest Neighbor (LNBNN) algorithm for the recognition of human face. We use Haar Like Features and the combination of AdaBoost algorithm and Cascade stratified model to detect and extract the face image, the DSIFT descriptors of the image are computed only for the aligned and cropped face image.Then, we apply the LNBNN algorithms for object recognition. Numerical testing on several benchmark datasets using our proposed method for facerecognition gives the better results than other methods. The accuracies obtained by LNBNN method is 99.74 %.
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Septyanto, Moh Wahyu, Herry Sofyan, Herlina Jayadianti, Oliver Samuel Simanjuntak, and Dessyanto Boedi Prasetyo. "APLIKASI PRESENSI PENGENALAN WAJAH DENGAN MENGGUNAKAN ALGORITMA HAAR CASCADE CLASSIFIER." Telematika 16, no. 2 (2020): 87. http://dx.doi.org/10.31315/telematika.v16i2.3182.
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AbstractPresence using face already widely adopted as a way of monitoring employee attendance. Research on using facial Presence never been done before by applying algorithms and algorithms Eigenface linear discriminant analysis (LDA). However, previous research has found that there are still weaknesses in the algorithms used. The weakness is that the process of identifying which takes a long time because the process of calculating the value carried on the overall image or image and the distance of the face of the webcam can affect the process of identifying faces. In this study, the algorithm used is haar cascade classifier algorithm. Haar classifier cascade or known by other names haar-like features are the rectangular features (square function), which gives an indication of the specifics on a picture or image. Principle Haar-like features are recognizing objects based on simple values of the features but not the pixel values of the object image. This method has the advantage that the computation is very fast, because it depends on the number of pixels in a square instead of each pixel value of an image. Haar classifier cascade also still be able to identify faces even if the distance face with the webcam is considerably due to the value of the facial features can still be identified. Results from this study that the system can identify the face with a good degree of accuracy. Tests carried out to 13 employees Starcross Store with each employee doing 30 times the experiment presence. Attendance successful has the success rate is 87% and 13% of the total failure of the experiment 390 times. Some absences failed to happen because there are several factors that can affect attendance as high luminance, uplifted head position, and the use of attributes (hats, glasses, etc.).Keywords : Presence, face recognition, Haar cascade classifier algorithmPresensi menggunakan wajah sudah banyak diterapkan sebagai cara untuk pemantauan kehadiran pegawai. Penelitian tentang presensi menggunakan wajah pernah dilakukan sebelumnya dengan menerapkan algoritma eigenface dan algoritma linear discriminant analysis (LDA). Namun dari penelitian sebelumnya telah ditemukan kelemahan yaitu pada proses pengidentifikasian yang membutuhkan waktu cukup lama dikarenakan proses perhitungan nilai dilakukan pada keseluruhan citra atau image dan jauhnya jarak wajah dari webcam dapat mempengaruhi proses pengidentifikasian wajah tersebut. Pada penelitian ini algoritma yang digunakan adalah algoritma haar cascade classifier. Haar cascade classifier atau yang dikenal dengan nama lain haar-like features merupakan rectangular features (fungsi persegi), yang memberikan indikasi secara spesifik pada sebuah gambar atau image. Prinsip Haar-like features adalah mengenali obyek berdasarkan nilai sederhana dari fitur tetapi bukan merupakan nilai piksel dari image obyek tersebut. Metode ini memiliki kelebihan yaitu komputasinya sangat cepat, karena hanya bergantung pada jumlah piksel dalam persegi bukan setiap nilai piksel dari sebuah image. Haar cascade classifier juga masih dapat mengidentifikasi wajah walaupun jarak wajah dengan webcam terbilang jauh dikarenakan nilai fitur wajah masih dapat diidentifikasi. Hasil dari penelitian ini bahwa sistem dapat mengidentifikasi wajah dengan tingkat akurasi baik. Pengujian dilakukan kepada 13 karyawan Starcross Store dengan masing-masing karyawan melakukan 30 kali percobaan presensi. Absensi yang berhasil memiliki nilai keberhasilan 87% dan 13% gagal dari total percobaan 390 kali. Beberapa absensi yang gagal terjadi karena ada beberapa faktor yang dapat mempengaruhi absensi seperti pencahayaan yang tinggi, posisi kepala yang mendongkak dan penggunaan atribut (topi, kacamata, dsb).Kata Kunci : Presensi, Pengenalan Wajah, Algoritma Haar Cascade Classifier
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Hashim, Siti, and Paul Mccullagh. "Face detection by using Haar Cascade Classifier." Wasit Journal of Computer and Mathematics Science 2, no. 1 (2023): 1–8. http://dx.doi.org/10.31185/wjcm.109.
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the Haar Cascade Classifier is a popular technique for object detection that uses a machine-learning approach to identify objects in images and videos. In the context of face detection, the algorithm uses a series of classifiers that are trained on thousands of positive and negative images to identify regions of the image that may contain a face. The algorithm is a multi-stage process that involves collecting training data, extracting features, training the classifiers, building the cascade classifier, detecting faces in the test image, and post-processing the results to remove false positives and false negatives. The algorithm has been shown to be highly accurate and efficient for detecting faces in images and videos, but it has some limitations, including difficulty in detecting faces under challenging lighting conditions or when the faces are partially occluded. Overall, the Haar Cascade Classifier algorithm remains a powerful and widely-used tool for face detection, but it is important to carefully evaluate its performance in the specific context of each application and consider using more advanced techniques when necessary.
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Al-Azzawy, Dhyaa Shaheed Sabr. "Application of Haar-like Features in Three AdaBoost Algorithms for Face Detection." Journal of Wasit for Science and Medicine 4, no. 1 (2022): 1–17. http://dx.doi.org/10.31185/jwsm.124.
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In this paper we introduce a proposed cascade classifier system consists of five strong classifiers (stages), and empirically analysis three types of Adaboost algorithms (Real, Modest, and Gentle) which it were used for boosting the strong classifiers. Five prototypes of haar-like features (two-horizontal rectangles, two-vertical rectangles, three-horizontal rectangles, three-vertical rectangles, and four-rectangles) are used for each strong classifier (stage) of cascade classifier. Two-horizontal, two-vertical rectangles features and Modest-Adaboost Algorithm are applied for the 1st and 2nd strong classifier respectively, three-horizontal, vertical-rectangles features and Real-Adaboost Algorithm are applied for 3rd and 4th strong classifier respectively, and the last, four-rectangles and Real-Adaboost Algorithm are applied for the 5th strong classifier of cascade classifier. The implementation shows that the best use of Adaboost algorithm is: the Modest-Adaboost algorithm for both 1st and 2nd strong classifier, and the Real-Adaboost algorithm for 3rd,4th and 5th strong classifier.
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Arju, Bano, Akash Saxena Dr., and Kumar Das Gaurav. "AN EFFICIENT DETECTION APPROACH OF DRIVER- DROWSINESS USING MULTIPLE CONVOLUTIONAL HAAR CASCADE KERNELIZED CNN (MCHCKCNN) ALGORITH." AN EFFICIENT DETECTION APPROACH OF DRIVER- DROWSINESS USING MULTIPLE CONVOLUTIONAL HAAR CASCADE KERNELIZED CNN (MCHCKCNN 02, no. 02 (2021): 165–71. https://doi.org/10.5281/zenodo.5060282.
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A lot of detail is transmitted by the face, an essential part of the body. If there is a car in a facial movement, for example, the frequency of yawning and blinking is distinct from that of fatigue state. It’s in its natural state. We suggest a new system to determine the standard of the driver. Centered on face monitoring and facial main point identification of fatigue. We are developing a new algorithm and proposing the Kernelized Convolutional Neutral Network Multiple Convolutional Haar Cascade (MCHC-KCNN) Algorithm for monitoring the face of the driver using CNN and MCHC and give 0.9827 accuracy to boost the original algorithm previously proposed algorithm. Haar-feature is similar to CNN kernel, except that values of a kernel in a CNN are defined by training, and Haar-feature is determined manually. We studied the fundamentals of face detecting and eye recognition with Haar Feature-based Cascade Classifiers in this article. At first, the algorithm requires a lot of good images (faces) and poor images (face-free images) to train the classification process. Then we must remove from it some features. Haar features seen in the image below are utilized for this purpose. They are just like our convolutional kernel which gives 0.9827 accuracy i.e. efficient and more than the previous approach. We have improved our model by employing an efficient optimizer, loss function and layers, which optimizes the algorithm in a complex setting, as low light, to enhance the latter’s efficiency.
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Tian, Xuejun, Haowen Feng, and Jieyan Chen. "An Industrial Production Line Dynamic Target Tracking System Based on HAAR and CAMSHIFT." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 11 (2020): 2059037. http://dx.doi.org/10.1142/s0218001420590375.
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Aiming at the detection and tracking of moving targets in industrial automation system, a dynamic target tracking algorithm based on HAAR and CAMSHIFT is proposed. A cascade HAAR classifier is designed and trained for tracking targets. CAMSHIFT algorithm is used to track and detect moving targets quickly. The system is tested on Raspberry Pi embedded platform. The results show that the algorithm can detect the target correctly and track the target effectively.
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Hall, Wayne, Andreas Kromik, Brenton Miller, Ian Underhill, and Zia Javanbakht. "A Machine Learning Model for Flaw Identification in Fibre-Reinforced Composites." Materials Science Forum 1094 (July 27, 2023): 5–10. http://dx.doi.org/10.4028/p-igdb3j.
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A Haar cascade classifier is a machine learning (ML) algorithm used for object detection. In this paper, the Haar algorithm is introduced in the context of a non-destructive evaluation of fibrereinforced composite (FRC) structures. The Haar learning model is used for flaw identification from thermal images. Thermal images are created from cross-ply (CP) carbon fibre-reinforced laminates with flat-bottomed holes (6–10 mm) of different depths from the surface (0.5–1.5 mm). After training is complete, the model successfully detects similar artificial flaws in previously unseen thermal images. In doing so, the feasibility of Haar classifiers for automatic evaluation of FRCs is established.
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Ashwini, K. Thokne, and N. Mandaogade Nitin. "Development of Improved Pose Independent Face Recognition Algorithm from Video." Research and Applications: Embedded System 5, no. 1 (2022): 1–6. https://doi.org/10.5281/zenodo.6472305.
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<em>In face detection, facial recognition is utilized as a biometric approach. Facial recognition is used to verify or identify a face in the multi-media pictures. Face recognition has become more crucial as society has become more technologically savvy. Face detection and recognition has spread around the globe. To the need for security, including authorization for national security or safety, and other critical factors. Situations. There are a variety of face recognition algorithms. The purpose of this study is to compare and contrast classified using Haar Cascade and Local Binary Pattern, two face-recognition methods. Consequently, Local Binary Pattern is more accurate, but Haar Cascade takes longer to run. Local binary pattern alone isn't enough.</em>
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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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Xiong, Hui Yun, and Juan Zhao. "An Image Retrieval Method Based on Machine Learning and SVM." Applied Mechanics and Materials 631-632 (September 2014): 474–77. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.474.
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Image recognition has been a research hotspot in the field of machine learning; this paper puts forward a kind of cascade algorithm based on SVM and AdaBoost. The algorithm to select the sample pretreatment, fixed size of window image segmentation into different areas, then using Haar - like rectangular figure characteristics of integral method for feature extraction, finally using AdaBoost cascade classifier to classify the SVM training. Through the face recognition experiments show AdaBoost cascade of SVM algorithm improve the classification accuracy, error rate get reduced obviously.
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G, Premalatha. "An Efficient Implementation of FPGA Based Face Detection and Face Recognition System Using haar Classifiers." International Journal of Computer Science and Engineering Communications 1, no. 1 (2013): 30–36. https://doi.org/10.5281/zenodo.821746.
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This paper introduces a novel technique to detect faces similarly recognizes in real-time with very high rate. It is essentially a feature-based approach, in which a classifier is trained for Haar-like rectangular features selected by Ada Boost algorithm and efficient representation method histogram equalization is used for varying illumination in the image. The face detection system generates an integral image window to perform a Haar feature classification during one clock cycle. And then it performs classification operations in parallel using Haar classifiers to detect a face in the image sequence. The classifiers in the beginning of the cascade are simpler and consist of smaller numbers of features. Although a face detection module is typically designed to deal with single images, its performance can be further improved if video stream is available.However, as one proceeds in the cascade, the classifiers become more complex. A region is reported as detection only if it passes all the classifier stages in the cascade. If it is rejected at any stage, it is discarded and not processed further. If all stages are passed the face of a candidate is concluded to be recognized face.
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Setyawan, Bambang Agus, and Mutaqin Akbar. "Detection of fake shallots using website-based haar-like features algorithm." Compiler 10, no. 2 (2021): 51. http://dx.doi.org/10.28989/compiler.v10i2.978.
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Shallots is commonly used as essential cooking spices or complement seasoning. The high market demand for this commodity has triggered some people to counterfeit it. They mix the shallots with defective products of onions to get more benefits. It urges to provide a system that can help people to distinguish whether the shallot is original or fake. This research aims to provides an object recognition system for fake shallots utilizing the Haar-Like Feature algorithm. It used the cascade training data set of 59 positive images and 150 negative images with 50 comparison images. The identification process of the shallots was through the haar-cascade process, integrated image, adaptive boosting, cascade classifier, and local binary pattern histogram. This system was made based on the Django website using the python programming language. The test was conducted 30 times on Brebes shallots mixed with Mumbai's mini onions in a single and mixture test method. The test obtained an average percentage of 69.2% for the object recognition of Mumbai's mini onions.
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Mohamed Hatim, Shahirah. "Drowsy Driver Detection Using Viola-Jones Algorithm." Mathematical Sciences and Informatics Journal 2, no. 2 (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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Rajavarshini, R., S. Shruthi, P. Mahanth, Boddu Chaitanya Kumar, and A. Suyampulingam. "Comparative analysis of image processing techniques for obstacle avoidance and path deduction." Journal of Physics: Conference Series 2070, no. 1 (2021): 012121. http://dx.doi.org/10.1088/1742-6596/2070/1/012121.
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Abstract The growing need for automation has a significant impact on our daily lives. Automating the essentials of our society like transportation system has plenty of applications like unmanned ground vehicles in military, wheel chair for disabled, domestic robots, etc., There are driving, braking, obstacle tackling etc., to a transportation system that can be automated. This paper particularly focuses on automating the obstacle avoidance which provides intelligence to the vehicle and ensures a high degree of safety and is performed using image processing algorithms. Edge based detection, image segmentation, and Machine Learning based method are the three image processing techniques used to detect and avoid obstacles. Haar cascade classifier is the machine learning method where Haar cascade analysis is performed for better accurate results with justifying graphs and parametric values obtained. A comparison of the three image processing algorithms is also tabulated considering obstacle size, colour, familiarities and environmental lightings and the best image processing algorithm is inferred.
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Febriana, Vivi, and Rosita Herawati. "PROPER FACE MASK DETECTION USING HAAR CASCADE." Proxies : Jurnal Informatika 4, no. 2 (2024): 136–48. http://dx.doi.org/10.24167/proxies.v4i2.12440.
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This project was created to detect the use of masks, where the use of masks is now the obligation of all communities, especially in public places. In this project, we will detect people who don't care and are not wearing masks properly. In detecting the use of masks, the Haar Cascade algorithm is used to detect facial, eye, nose, and mouth objects. There are 3 libraries to help detect masks such as haarcascade_frontalface_default.xml to detect face objects from the front side, haarcascade_eye.xml to detect eye objects, Nariz.xml to detect nose objects, and haarcascade_mcs_mouth.xml to detect mouth objects. From the image obtained, it will be converted to grayscale and then black and white to be able to detect faces, eyes, noses, and mouths. To analyze the results of mask detection, it is done by using a video containing image data of the use of masks. There are a total of 125 data to measure the level of accuracy in the mask detection program. The results obtained with an average accuracy level is 89,5%.
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N., Dileep kumar, and Shanthi S. "Automatic Gate using Face Recognition Technique using HAAR Cascade Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 1302–5. https://doi.org/10.35940/ijeat.C5195.029320.
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Now a day, in every single person households it is important to check regularly regarding their safety. Especially for elderly people it is mandatory, because they have become a target for certain burglars which leads to higher accidents/robberies in almost all the areas. To decrease the risk of such unwanted happenings in living space for single-person households, the hybrid security system should be adopted. The automatic personal identification has become the popular instead of using passwords or pattern in this days. This paper addresses the development of a face recognition technique for the above mentioned purpose.
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PM, Sajid, Kshithy Ravindran, Dharsana C, Sreeram CV, and Manoj M. "Automated Invigilation System Using MediaPipe and Haar Cascade Frontal Algorithm." June 2023 5, no. 2 (2023): 210–22. http://dx.doi.org/10.36548/jitdw.2023.2.010.
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Exams are the methods adopted by educational institutions to identify student’s knowledge. Students adopt various ways to cheat in exams like answer sheet exchanging, copying etc, students cheat their way into getting good grades. Detection of cheating manually may not be efficient to identify and prevent cheating during examinations. So, to avoid this the process of invigilation is made automatic. Automated invigilation offers the best method for keeping an eye on the kids and spotting instances of malpractice right away. The proposed work has three phases. In the first phase, the exam management does processes like publishing time table, allocating exam hall, allocating hall to staff etc. In the second phase the posture detection of the student present in the exam hall is done using Computer Vision and Media Pipe to detect whether the student has involved in the malpractice. In the third phase, the emotion analysis and face recognition of the student is done using the Haar Cascade Frontal Algorithm. The proposed work also helps to eliminate impersonation in the exam hall.
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S, Kishore, Mathi Shankar S, and Joncy J. "WiFi Enabled in Campus Surveillance System Using HAAR Cascade Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 3613–15. http://dx.doi.org/10.22214/ijraset.2023.54358.
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Abstract: This paper focuses on the development of Wifi Enabled in Campus Surveillance System Using Haar Cascade Algorithm. The system captures and stores images of individuals accessing a building and matches them against a pre-existing database of authorized personnel. If a match is found, the system logs the entry time. Similarly, during exit, the same process will be followed. Unlike access cards or passwords, which can be easily stolen or shared, a person's face cannot be replicated or forged, making it a more reliable means of identification and identify any security breaches. With this system, authorized personnel can simply walk into the building, and their entry is automatically recorded. Overall, This system utilizing face recognition technology is a powerful security solution that offers enhanced security, improved efficiency, and real-time monitoring capabilities. Its ability to authenticate individuals based on their facial features is making it an attractive option for organizationslooking to improve their security.
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J Jagadeesan, M. Azhagiri, and M. Gowtham Sethupathi. "Touchless ATM Using Augmented Reality Using TOTP Haar Cascade Algorithm." International Journal of Soft Computing and Engineering 15, no. 1 (2025): 5–8. https://doi.org/10.35940/ijsce.f3506.15010325.
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Touchless ATMs, a new technology, offer a contactfree, hygienic, and convenient financial transaction experience. This innovative solution uses Augmented Reality (AR), Timebased One-Time Passwords (TOTP), and the HAAR Cascade Algorithm to create an interactive virtual interface, reducing physical contact and enhancing transaction security. The system uses a dual-layered authentication mechanism, utilizing facial recognition and time-based, one-time passwords (TOTP) to validate user identities and generate dynamic, session-specific codes. Financial institutions can deploy this system to upgrade their ATM networks, catering to diverse user demographics. Challenges include developing robust gesture recognition models, ensuring low latency in AR interactions, and integrating these advanced technologies into existing ATM infrastructures. However, advances in hardware and software, coupled with the decreasing cost of AR and machine learning technologies, make this solution viable and scalable.
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M., Azhagiri. "Touchless ATM Using Augmented Reality Using TOTP Haar Cascade Algorithm." International Journal of Soft Computing and Engineering (IJSCE) 15, no. 1 (2025): 5–9. https://doi.org/10.35940/ijsce.F3506.15010325.
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<strong>Аbstrасt: </strong>Touchless ATMs, a new technology, offer a contactfree, hygienic, and convenient financial transaction experience. This innovative solution uses Augmented Reality (AR), Timebased One-Time Passwords (TOTP), and the HAAR Cascade Algorithm to create an interactive virtual interface, reducing physical contact and enhancing transaction security. The system uses a dual-layered authentication mechanism, utilizing facial recognition and time-based, one-time passwords (TOTP) to validate user identities and generate dynamic, session-specific codes. Financial institutions can deploy this system to upgrade their ATM networks, catering to diverse user demographics. Challenges include developing robust gesture recognition models, ensuring low latency in AR interactions, and integrating these advanced technologies into existing ATM infrastructures. However, advances in hardware and software, coupled with the decreasing cost of AR and machine learning technologies, make this solution viable and scalable.
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Arora, Mehul, Sarthak Naithani, and Anu Shaju Areeckal. "A web-based application for face detection in real-time images and videos." Journal of Physics: Conference Series 2161, no. 1 (2022): 012071. http://dx.doi.org/10.1088/1742-6596/2161/1/012071.
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Abstract Face detection is widely used in the consumer industry such as advertising, user interfaces, video streaming apps and in many security applications. Every application has its own demands and constraints, and hence cannot be fulfilled by a single face detection algorithm. In this work, we developed an interactive web-based application for face detection in real-time images and videos. Pretrained face detection algorithms, namely Haar cascade classifier, HOG-based frontal face detector, Multi-task Cascaded Convolutional Neural Network (MTCNN) and Deep Neural Network (DNN), were used in the web-based application. A performance analysis of these face detection algorithms is done for various parameters such as different lighting conditions, face occlusion and frame rate. The web app interface can be used for an easy comparison of different face detection algorithms. This will help the user to decide on the algorithm that suits their purpose and requirements for various applications.
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Mentari, Mustika, Rosa Andrie Asmara, Kohei Arai, and Haidar Sakti Oktafiansyah. "Detecting Objects Using Haar Cascade for Human Counting Implemented in OpenMV." Register 9, no. 2 (2023): 122–33. http://dx.doi.org/10.26594/register.v9i2.3175.
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Sight is a fundamental sense for humans, and individuals with visual impairments often rely on assistance from others or tools that promote independence in performing various tasks. One crucial aspect of aiding visually impaired individuals involves the detection and counting of objects. This paper aims to develop a simulation tool designed to assist visually impaired individuals in detecting and counting human objects. The tool's implementation necessitates a synergy of both hardware and software components, with OpenMV serving as a central hardware device in this study. The research software was developed using the Haar Cascade Classifier algorithm. The research process commences with the acquisition of image data through the OpenMV camera. Subsequently, the image data undergoes several stages of processing, including the utilization of the Haar Cascade classifier method within the OpenMV framework. The resulting output consists of bounding boxes delineating the detection areas and the tally of identified human objects. The results of human object detection and counting using OpenMV exhibit an accuracy rate of 71%. Moreover, when applied to video footage, the OpenMV system yields a correct detection rate of 73% for counting human objects. In summary, this study presents a valuable tool that aids visually impaired individuals in the detection and counting of human objects, achieving commendable accuracy rates through the implementation of OpenMV and the Haar Cascade Classifier algorithm.
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Andrean, Muhammad Niko, Guruh Fajar Shidik, Muhammad Naufal, et al. "Comparing Haar Cascade and YOLOFACE for Region of Interest Classification in Drowsiness Detection." JURNAL MEDIA INFORMATIKA BUDIDARMA 8, no. 1 (2024): 272. http://dx.doi.org/10.30865/mib.v8i1.7167.
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Driver drowsiness poses a serious threat to road safety, potentially leading to fatal accidents. Current research often relies on facial features, specific eye components, and the mouth for drowsiness classification. This causes a potential bias in the classification results. Therefore, this study shifts its focus to both eyes to mitigate potential biases in drowsiness classification.This research aims to compare the accuracy of drowsiness detection in drivers using two different image segmentation methods, namely Haar Cascade and YOLO-face, followed by classification using a decision tree algorithm. The dataset consists of 22,348 images of drowsy driver faces and 19,445 images of non-drowsy driver faces. The segmentation results with YOLO-face prove capable of producing a higher-quality Region of Interest (ROI) and training data in the form of eye images compared to segmentation results using the Haar Cascade method. After undergoing grid search and 10-fold cross-validation processes, the decision tree model achieved the highest accuracy using the entropy parameter, reaching 98.54% for YOLO-face segmentation results and 98.03% for Haar Cascade segmentation results. Despite the slightly higher accuracy of the model utilizing YOLO-face data, the YOLO-face method requires significantly more data processing time compared to the Haar Cascade method. The overall research results indicate that implementing the ROI concept in input images can enhance the focus and accuracy of the system in recognizing signs of drowsiness in drivers.
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Nazarkevych, Mariia, Vasyl Lytvyn, and Victoria Vysotska. "METHOD OF RECOGNITION OF MOVING OBJECTS BASED ON THE CLASSIFICATION OF HAAR CASCADES." Cybersecurity: Education, Science, Technique 2, no. 26 (2024): 361–73. https://doi.org/10.28925/2663-4023.2024.26.698.
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A method of recognition of moving objects in a video stream based on the Haar classification has been developed. When tracking objects, there is a need to identify them and record their direction of movement, speed of movement. The complexity of recognition lies not only in fixing the object and following it, but also in the movement of the camera itself, from which video surveillance is conducted. The Haar method is based on cascade classifiers that quickly highlight regions with a high probability of detecting an object. Haar cascades use a convolution operation, which is formed on the basis of the proportional product of Fourier images of functions. The disadvantages of Haar cascades include the fact that recognition is unstable when lighting changes, unstable with changes in scale and rotation of key frames. When implementing this method, no one changes the backgrounds in the video sequences. This method is very fast to implement, and accordingly the least accurate, compared to SURF and SIFT. However, it is accessible to programming and free to use. The Adaboost classifier was used to apply Haar Cascades. This algorithm selects a small number of significant features from a larger set to provide an effective result. Adaboost is an ensemble learning method that belongs to the category of boosting algorithms, which allows combining decision tree models with a small depth to create a strong model capable of providing high accuracy of classification or regression. In addition to object recognition, a machine learning method based on supervised methods was implemented to implement object location prediction and object identification. The training sample included military vehicles btr, bmp, tank, car and howitzer. It is planned to use random forest, SVM, gradient boosting and neural networks algorithms for object identification. The metrics of machine learning results are considered, in particular, the accuracy, completeness, F1-score, Kappa coefficient, and error matrix. The developed models are evaluated. In the future, it is planned to improve the methods that have been started.
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Archana, Balkrishna Yadav. "Towards Real-Time Facial Emotion-Based Stress Detection Using CNN and Haar Cascade in AI Systems." International Journal of Engineering and Management Research 14, no. 5 (2024): 83–88. https://doi.org/10.5281/zenodo.14064731.
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Understanding human conduct requires the ability to recognise facial emotions, which has applications in everything from human-computer interaction to psychological wellness monitoring. This research provides a new approach to stress detection using Convolutional Neural Networks (or CNNs) and HaarCascade classifiers. The suggested method uses a CNN to recognise facial expressions and Haar Cascade algorithm for face detection. The methodology begins with preliminary processing the input photos, followed by face detection and extraction of facial regions. Those parts are then fed into the CNN model, which classifies emotions. The system has been trained and tested on publicly available datasets, with encouraging results in stress detection accuracy. This method, which detects stress through facial expressions, has potential uses in stress management, mental health evaluation, and personalised therapies. Face expressions have an important part in transmitting emotions, especially stress, which is a common problem in today's fast-paced world. This research provides a novel approach for detecting stress by analysing facial expressions with Convolutional Neural Networks(CNNs)and Haar Cascade classifiers. The proposed system enhances the precision and effectiveness of stress detection by combining the benefits of both approaches. The methodology begins by preprocessing the input photos to improve their quality and normalise them for subsequent analysis. Haar Cascade classifiers are then used to detect faces in the images, ensuring precise identification of facial regions even under different lighting conditions and orientations. The discovered faces are removed and resized to produce homogeneous inputs for further processing.
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E, Subash, Hariprasath M, Aathithya S, et al. "Attendance Management System Using Face Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43195.
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This paper presents a Hybrid Multi-Stage Face Detection Algorithm that integrates traditional and deep learning methods for improved accuracy and efficiency. The process begins with Preprocessing and Enhancement to refine image quality. Fast Face Candidate Selection (Haar + HOG + SVM) quickly detects potential faces, followed by Precise Localization using MTCNN to refine detections and extract facial landmarks. Deep Learning Verification (RetinaFace/YOLO) eliminates false positives, ensuring reliability. Finally, Face Tracking (Kalman Filter + SORT) maintains consistency in video streams. This approach provides a robust and adaptable solution for real-world face detection applications. Keywords : Face Detection, Hybrid Algorithm, Deep Learning, Haar Cascade, HOG + SVM, MTCNN, RentinaFace, YOLO, Face Tracking, Real-Time Processing
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Mensah, Daniel Antwi, and Emmanuel Djaba. "Autonomous Road Crossing Surveillance System." Advances in Multidisciplinary and scientific Research Journal Publication 29 (December 15, 2021): 171–78. http://dx.doi.org/10.22624/aims/abmic2021-v2-p13.
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Accidents related with vulnerable pedestrians around crosswalks are continued so that proactive safety support system is required. Pedestrian detection from frames captured by a camera is a significant and yet challenging task. An autonomous road crossing surveillance system would be ideal for tracking pedestrians who want to cross the road and assist them. A practical solution for aiding pedestrians regularly, a road crossing surveillance with real-time Pedestrian Detection. Since the background subtraction from videos and images is still a persistent problem and difficult to accomplish. A Haar Cascade Classifier with the full-body detection algorithm is used to detect people in real-time captured by a camera. Keywords: Pedestrian crossing, surveillance, road crossing surveillance, surveillance system, autonomous, Haar Cascade Classifier.
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P., Devi Mahalakshmi, and M. Babu Dr. "Vehicle Speed Estimation using Haar Classifier Algorithm." International Journal of Trend in Scientific Research and Development 4, no. 1 (2019): 243–46. https://doi.org/10.5281/zenodo.3604816.
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An efficient traffic management system is needed in all kinds of roads, such as off roads, highways, etc... Though several laws and speed controller has been attached to the vehicles, Speed limit may vary from road to road. Still Traffic management system faces different kinds of challenges everyday and its being a research area though number of proposals has been identified. Many numbers of methods has been proposed in computer Vision and machine learning approaches for object tracking. In this paper vehicles are identified and detected using a videos that taken from surveillance camera. The objective of the present work is to identification of the vehicles is done by using Computer vision technique and detection of vehicles using Haar cascade classifier. Detecting the vehicles using machine learning and estimating speed is tough but beneficial task. For the past few tears Convolution Neural Network CNN has been widely used in computer vision for vehicle detection and identification. This method manages to track multiple objects at real time using dlibs. P. Devi Mahalakshmi | Dr. M. Babu "Vehicle Speed Estimation using Haar Classifier Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29482.pdf
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Ihsan, Indah Purwitasari. "Haar Cascade dan Algoritma Eignface Untuk Sistem Pembuka Pintu Otomatis." JSAI (Journal Scientific and Applied Informatics) 4, no. 2 (2021): 182–92. http://dx.doi.org/10.36085/jsai.v4i2.1642.
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Teknologi diciptakan untuk mempermudah manusia dalam melakukan segala pekerjaan dan aktifitasnya, termasuk dalam hal mengakses pintu. Menggunakan teknologi pengolahan citra, wajah merupakan salah satu alternatif yang bisa digunakan untuk mengakses pintu dan mengamankannya dari orang yang tidak bertanggung jawab. Hal ini dikarenakan wajah setiap manusia memiliki pola yang berbeda-beda yang bisa ditransformasikan menjadi citra digital dan diolah mengunakan algoritma pengolahan citra. Dalam penelitian ini, mengkombinasikan haar cascade dan algoritma eigenface untuk mengolah citra wajah. Hasil dari pengolahan citra tersebut digunakan untuk menentukan hak akses dalam mengakses pintu, untuk kemudian diintegrasikan ke mikrokontroller, sehingga pintu dapat terbuka otomatis. Penelitian ini menghasilkan prototype system pembuka pintu otomatis dengan pengenalan wajah sebagai penentu hak aksesnya. Dari hasil penelitian, algoritma eigenface tidak dapat bekerja pada pencahayaan 0 lux hingga 8 lux dalam jarak 20 cm hinga 60 cm yaitu menghasilkan akurasi 0%, sedangkan pada pencahayaan 36 lux sampai 44 lux dan 160 lux sampai172 lux algoritma eigenface bekerja dengan baik dengan jarak pengambilan gambar 20-60 cm dengan akurasi 80%. Technology was created to make it easier for humans to do all their work and activities, including accessing doors. Using image processing technology, faces are an alternative that can be used to access doors and secure them from irresponsible people. This is because the face of every human being has a different pattern that can be transformed into a digital image and processed using an image processing algorithm. In this research, combining haar cascade and eigenface algorithm to processing face images. The results of the image processing are used to determine access rights in accessing the door, and then integrated into the microcontroller, so that the door can be opened automatically. This research produces a prototype automatic door opening system with face recognition as a determinant of access rights. From the results of the study, the eigenface algorithm cannot work at 0 lux to 8 lux lighting within a distance of 20 cm to 60 cm which produces 0% accuracy, while at 36 lux to 44 lux and 160 lux to 172 lux lighting the eigenface algorithm works well with a shooting distance of 20 cm to 60 cm with 80% accuracy.
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Phuc, Le Tran Huu, HyeJun Jeon, Nguyen Tam Nguyen Truong, and Jung Jae Hak. "Improving the Dipping Step in Czochraski Process Using Haar-Cascade Algorithm." Electronics 8, no. 6 (2019): 646. http://dx.doi.org/10.3390/electronics8060646.
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Czochralski crystal growth has become a popular technique to produce pure single crystals. Many methods have also been developed to optimize this process. In this study, a charge-coupled device camera was used to record the crystal growth progress from beginning to end. The device outputs images which were then used to create a classifier using the Haar-cascade and AdaBoost algorithms. After the classifier was generated, artificial intelligence (AI) was used to recognize the images obtained from good dipping and calculate the duration of this operating. This optimization approach improved a Czochralski which can detect a good dipping step automatically and measure the duration with high accuracy. Using this development, the labor cost of the Czochralski system can be reduced by changing the contribution of human specialists’ mission.
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Wang, Hong, Xian Li, and Shuang Liu. "The Design of a Car License Plate Identification System Based on AdaBoost Algorithm." Advanced Materials Research 181-182 (January 2011): 588–93. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.588.
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Design and implement a car license plate identification system with the applications of Viola and Jones algorithm. This algorithm which is based on the AdaBoost method is trained and optimized for the best performance using large database of car license plate images. The final license plate identification system obtained a cascade of classifiers consisting of 8 stages with 1310 Haar-like features. Once the license plates have sufficient visibility and there are no other objects similar to the plate in images, this system operates perfectly and shows high correct identification rate with low false positive rate. And as integral image allows the Haar-like features to be calculated very fast, the system also finished the identification rapidly.
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Damarsiwi, Dyah Kartika, Elindra Ambar Pambudi, Maulida Ayu Fitriani, and Feri Wibowo. "Face Detection in Complex Background using Scale Invariant Feature Transform and Haar Cascade Classifier Methods." Sinkron 8, no. 2 (2024): 852–60. http://dx.doi.org/10.33395/sinkron.v8i2.13556.
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Face detection is a process by a computer system that can find and identify human faces in digital images or videos. One of the main challenges faced in the face detection process is the complex background. Complex backgrounds, such as many color combinations in the image, can interfere with the detection process. To overcome this challenge, this research uses a combination of two methods: Scale Invariant Feature Transform (SIFT) and Haar Cascade Classifier. Scale Invariant Feature Transform (SIFT) is a method used in image processing to identify and describe unique features in an image. The SIFT method looks for keypoint descriptors in images that can be used as a reference in comparing different images. After the keypoint descriptor is found with SIFT, the Haar Cascade Classifier method is used to detect faces in the image. Haar Cascade Classifier is a practical algorithm for object detection in images. After facial features are extracted with these two methods, the results are compared with the K-Nearest Neighbor (KNN) approach. This research involves the introduction of 28 color images with complex backgrounds. The results of combining these two methods produce an accuracy of 81.75%. This shows that combining these two methods effectively overcomes complex background challenges in face detection.
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Firasari, Elly, F. Lia Dwi Cahyanti, Fajar Sarasati, and Widiastuti Widiastuti. "COMPARISON OF EIGENFACE AND FISHERFACE METHODS FOR FACE RECOGNITION." Jurnal Techno Nusa Mandiri 19, no. 2 (2022): 125–30. http://dx.doi.org/10.33480/techno.v19i2.3470.
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Abstract— Biometric information systems have been widely used in the fields of government, shopping centers, education and even security, which offer biological authentication so that the system can recognize its users more quickly. The parts of the human body are identified by a biometric system that has unique and specific characteristics, one of which is the face. Adjustment of facial image deals with objects that are never the same, due to the parts that can change. These changes are caused by facial expressions, light intensity, shooting angle, or changes in facial accessories. With this, the same object with several differences must be recognized as the same object. In this study, the data used were 388 face images and the sata test consisted of 30 face images. Before the face is tested, preprocessing and feature extraction are carried out using the Haar Cascade Classifier and then detected using Eigenface and Fisherface. Based on the research results, the Fisherface method is an algorithm that is accurate and efficient compared to the Eigenface algorithm. The Fisherface algorithm has an accuracy of 88%. while the Eigenface method has an accuracy rate of 76%.
 Keywords – Haar Cascade Classifier, Eigenface, Fisherface,.
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Dirgantara, F. M., and D. P. Wicaksa. "Design of Face Recognition Security System on Public Spaces." Journal of Electrical, Electronic, Information, and Communication Technology 4, no. 1 (2022): 6. http://dx.doi.org/10.20961/jeeict.4.1.60409.
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<p class="Abstract">Implementation of physical security such as recruitment of security officers, installation of CCTV, and restrictions on public access have become commonplace nowadays. Computer systems equipped with archival storage media must be properly maintained, including computer systems containing sensitive information that must be stored in a locked and secure place. This study applies a security system using facial recognition to determine who is authorized over the data in the computer system—using Haar Cascade as a face detector and LBPH as a match between faces that can access and those that are not on the list. On the other hand, if the person is unidentified, preventive measures will be performed. Based on the result, the proposed system using Raspberry Pi 4 is able to identify a face using Haar Cascade algorithm with an accuracy of 68% and average duration process of 0.392s, and able to recognize face using LBPH algorithm with accuracy between 50.74% to 100% and average duration process of 0.548s.<em></em></p>
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Azzahra, Fidela, Christy Atika Sari, and Eko Hari Rachmawanto. "The AirNav Semarang Employee Presence System Using Face Recognition Based on Haar Cascade." Advance Sustainable Science Engineering and Technology 6, no. 3 (2024): 02403011. http://dx.doi.org/10.26877/asset.v6i3.672.
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The presence of employees is a key factor in supporting the needs of the workplace. At present, the employee presence system at PT. AirNav Indonesia Semarang Branch still uses fingerprint and RFID-based employee ID cards for authentication. This RFID-based system can increase employee fraud by allowing employees to misuse each other's ID cards. To avoid such fraud, a system needs to be built and it will be using face recognition technology as the primary authentication method, with the Haar Cascade Algorithm. This algorithm has the advantage of being computationally fast, as it only relies on the number of pixels within a rectangle, not every pixel of an image. In addition to fast computation, this algorithm also has the advantage of identifying objects that are relatively far away. With the implementation of the Haar Cascade algorithm, the results indicate the capability of face recognition in detecting the faces of registered employees within the system based on facial angles with an accuracy rate of 60%, expressions with an accuracy rate of 100%, as well as obstructive parameters such as glasses and masks with an accuracy rate of 33.33%. The ability to detect objects from various camera angles, recognize faces with different expressions, and identify objects obstructed by parameters can serve as reasons why this algorithm needs to be implemented
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Samsinar, Riza, Idhar Mahasen, and Anwar Ilmar Ramadhan. "Identifikasi Penggunaan Masker untuk Pencegahan Covid-19 dengan Metode Local Binary Pattern Histogram (LBPH) dan Metode Haar Cascade Classifier." RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) 5, no. 2 (2022): 151. http://dx.doi.org/10.24853/resistor.5.2.151-156.
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Penerapan protokol kesehatan khususnya menggunakan masker dalam konteks pandemi Covid-19 masih banyak dilanggar oleh sebagian masyarakat. Hal ini dikarenakan kurangnya pengawasan terhadap kebijakan tersebut. Untuk mendukung penerapan protokol kesehatan dapat digunakan LBPH (Local Binary Pattern Histogram) yang merupakan metode pengenalan wajah, ditambah dengan Haar Cascade Classifier yang merupakan algoritma pengenalan objek untuk mendeteksi wajah dan bagian-bagian wajah seperti hidung dan mulut, kedua metode tersebut dapat dikolaborasikan dengan bahasa pemrograman python. Metode LBPH dapat bekerja dengan baik pada resolusi minimal 240 px dengan tingkat keberhasilan sebesar 80%. Sedangkan untuk mendeteksi bagian-bagian wajah (hidung dan mulut), Haar Cascade Classifier menunjukkan tingkat keberhasilan sebesar 90% dan keberhasilan deteksi penggunaan masker 90%. Pada penerapannya sistem ini memerlukan pengaturan scale factor dan miminum neighbor serta penyesuaian citra latih dan citra saat implementasi untuk mendapatkan hasil yang maksimal. The implementation of health protocols, especially the use of masks in the context of the Covid-19 pandemic, is still widely violated by some people. This is due to the lack of oversight of the policy. To support the implementation of health protocols, can be used LBPH (Local Binary Pattern Histogram) which is a facial recognition method, coupled with the Haar Cascade Classifier which is an object recognition algorithm to detect faces and parts of the face such as nose and mouth, both methods can be collaborated with language python programming. The LBPH method can work well at a minimum resolution of 240 px with a success rate of 80%. Meanwhile, to detect parts of the face (nose and mouth), the Haar Cascade Classifier shows a 90% success rate and 90% success in detecting the use of masks. In practice this system requires setting scale factor and minimum neighbor as well as image adjustment and image training during implementation to obtain maximum results.
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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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Hustinawaty and Muhammad Farell. "Implementation of Mask Use Detection With SVM and Haar Cascade in OpenCV." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 13, no. 1 (2024): 31–37. http://dx.doi.org/10.22146/jnteti.v13i1.9292.
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Despite a decline in global COVID-19 cases, the persisting threat of SARS-CoV-2 coupled with waning public awareness of the virus threat has raised concerns. A notable number of individuals disregard mask usage or do so incorrectly. It is particularly concerning given that COVID-19 has high transmissibility, especially in crowded areas like shopping centers. Enforcement officers often face challenges in identifying those wearing masks improperly. Herein lies the significance of automated mask detection to aid enforcement officers in containing the spread of the virus. Hence, this paper aims to highlight the importance of automated mask detection in combatting COVID-19 transmission. Previous mask detection algorithms were intricate because they relied heavily on resource-intensive machine learning algorithms and libraries. These algorithms, however, failed to address the problem of incorrect mask usage adequately. Therefore, despite the apparent usage of masks, the virus managed to find transmission pathways. In contrast, this research focuses on creating algorithms that pinpoint improper mask usage and optimize resource utilization without compromising detection quality. The Haar cascade algorithm was utilized to detect faces and the support vector machine (SVM) was used to train the dataset. The model attained an average accuracy of 95.8%, precision of 99.7%, recall of 92.3%, and F1-score of 93.7%. The metrics aligned with prior studies, affirming their reliability. Nevertheless, limitations exist as the model faces challenges in detecting obscured facial features, requiring further research to enhance its detection capabilities. This research contributes to ongoing efforts to improve mask detection technology for more effective virus containment.
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Adeshina, Sirajdin Olagoke, Haidi Ibrahim, Soo Siang Teoh, and Seng Chun Hoo. "Custom Face Classification Model for Classroom Using Haar-Like and LBP Features with Their Performance Comparisons." Electronics 10, no. 2 (2021): 102. http://dx.doi.org/10.3390/electronics10020102.
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Face detection by electronic systems has been leveraged by private and government establishments to enhance the effectiveness of a wide range of applications in our day to day activities, security, and businesses. Most face detection algorithms that can reduce the problems posed by constrained and unconstrained environmental conditions such as unbalanced illumination, weather condition, distance from the camera, and background variations, are highly computationally intensive. Therefore, they are primarily unemployable in real-time applications. This paper developed face detectors by utilizing selected Haar-like and local binary pattern features, based on their number of uses at each stage of training using MATLAB’s trainCascadeObjectDetector function. We used 2577 positive face samples and 37,206 negative samples to train Haar-like and LBP face detectors for a range of False Alarm Rate (FAR) values (i.e., 0.01, 0.05, and 0.1). However, the study shows that the Haar cascade face detector at a low stage (i.e., at six stages) for 0.1 FAR value is the most efficient when tested on a set of classroom images dataset with 100% True Positive Rate (TPR) face detection accuracy. Though, deep learning ResNet101 and ResNet50 outperformed the average performance of Haar cascade by 9.09% and 0.76% based on TPR, respectively. The simplicity and relatively low computational time used by our approach (i.e., 1.09 s) gives it an edge over deep learning (139.5 s), in online classroom applications. The TPR of the proposed algorithm is 92.71% when tested on images in the synthetic Labeled Faces in the Wild (LFW) dataset and 98.55% for images in MUCT face dataset “a”, resulting in a little improvement in average TPR over the conventional face identification system.
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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 (2021): 3218–22. http://dx.doi.org/10.22214/ijraset.2021.37021.
Full textAbstract:
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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Jemakmun, Jemakmun makmun, and Rudy Suhirja. "Haar Cascade Algorithm On Mask Detection System Based On Distance In Facing The Normal Era." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 7, no. 1 (2023): 84–90. http://dx.doi.org/10.31289/jite.v7i1.9346.
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The COVID-19 outbreak has hit almost the whole world, including Indonesia which has become a disease outbreak in early 2020. Therefore, currently, various places have enforced regulations to comply with health protocols by using masks. So all South Sumatra must follow health protocols by wearing masks and maintaining distance. So the program for making this Mask Detection System is one way to overcome public awareness, especially among Bina Darma University students about the importance of using masks today. In the case of making this mask detection system program, the researchers used Python and the Haar Cascade Algorithm. From experiments using the Haar Cascade method, this system can detect people who use masks and do not use masks. This test is also done by inputting images or videos. The results of the study that, based on distance and angle, the estimated minimum distance for this mask detection application is 25 cm and the maximum is 150 cm will produce maximum mask detection results and based on distance, the estimated distance is 50 cm to 300 cm, the design of the detection system can recognize the maximum face
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Dr., B. Sujatha, and Ramya R. "An Advanced Facial Emotional Recognition Using HAAR Cascade Algorithm in Machine Learning." International Research Journal of Computer Science 10, no. 06 (2023): 363–66. http://dx.doi.org/10.26562/irjcs.2023.v1006.17.
Full textAbstract:
Interpersonal relationships rely heavily on facial expression-based nonverbal cues. A typical part of human-machine points of interaction is the programmed acknowledgment of looks; It could also be used in behavioral science and clinical practice. Despite the fact that people see facial expressions almost immediately, machine expression recognition still faces challenges. From the point of view of automatic recognition, a facial expression can be thought of as changes in the pigmentation of the face or disfigurements of the facial parts and their spatial relationships. Automatic facial expression recognition research focuses on the issues surrounding the representation and arrangement of static or dynamic qualities of these distortions or face pigmentation.
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Tian, M. Z., L. Liu, J. Y. Lu, and Y. Cheng. "Vehicle recognition based on Haar features and Adaboost cascade classifier." Journal of Physics: Conference Series 2303, no. 1 (2022): 012052. http://dx.doi.org/10.1088/1742-6596/2303/1/012052.
Full textAbstract:
Abstract With the progress of world science and economic development, more and more industries are developing towards intelligence and automation. In the field of intelligent driving, the intelligent vehicle environment perception method based on machine vision has become a hot research topic. Based on monocular vision system, aiming at the requirements of different target features and detection accuracy and efficiency, this paper improves the Haar feature and Adaboost cascade classifier recognition algorithm combined with gray symmetry method to adapt to the recognition environment required by vehicles. The measured results show that the improved vehicle identification method combined with the tracking method based on Kalman filter can reduce the misjudgment rate of vehicles and has good real-time performance.