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Relevant bibliographies by topics / HAAR cascade algorithm

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Academic literature on the topic 'HAAR cascade algorithm'

Author: Grafiati

Published: 5 June 2025

Last updated: 24 June 2025

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Journal articles on the topic "HAAR cascade algorithm"

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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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Dissertations / Theses on the topic "HAAR cascade algorithm"

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Nagavelli, Sai Krishnanand. "Improve Nano-Cube Detection Performance Using A Method of Separate Training of Sample Subsets." Youngstown State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1485267005121308.

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Pereira, Rafael Cardoso. "T?cnica de rastreamento e persegui??o de alvo utilizando o algoritmo Haar cascade aplicada a rob?s terrestres com restri??es de movimento." PROGRAMA DE P?S-GRADUA??O EM ENGENHARIA MECATR?NICA, 2017. https://repositorio.ufrn.br/jspui/handle/123456789/23739.

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Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-08-01T15:43:01Z No. of bitstreams: 1 RafaelCardosoPereira_DISSERT.pdf: 1817897 bytes, checksum: 903100b393275d014c5095608b4e8e81 (MD5)<br>Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-08-09T13:54:55Z (GMT) No. of bitstreams: 1 RafaelCardosoPereira_DISSERT.pdf: 1817897 bytes, checksum: 903100b393275d014c5095608b4e8e81 (MD5)<br>Made available in DSpace on 2017-08-09T13:54:55Z (GMT). No. of bitstreams: 1 RafaelCardosoPereira_DISSERT.pdf: 1817897 bytes, checksum: 903100b393275d014c5095608b4e8e81 (MD5) Previous issue date: 2017-06-21<br>A habilidade de seguir ou de se mover acompanhando uma pessoa ou um objeto especificado, capaz de se deslocar, ? uma per?cia necess?ria em diversos agentes aut?nomos. Tais agentes s?o amplamente utilizados para realizar v?rias tarefas presentes no cotidiano, podendo ser aplicados tanto em tarefas corriqueiras, como em carrinhos de supermercado ou limpeza de ambientes, quanto ?s tarefas de mais alto risco, como em grandes ind?strias ou carros aut?nomos. A ideia apresentada aqui ? a de desenvolver um m?todo de rastreamento e persegui??o de alvo aplic?vel ? rob?s m?veis terrestres com rodas que possuem restri??es em sua movimenta??o, que fazem com que t?cnicas de controle padr?o nem sempre possam ser aplicadas. O trabalho desenvolvido aqui tamb?m leva em considera??o a utiliza??o de uma t?cnica de detec??o de alvo que possa se tornar adapt?vel a praticamente qualquer tipo de alvo estipulado pelo projetista de acordo com as necessidades de sua aplica??o. O desenvolvimento dos m?todos propostos foram realizados agregando t?cnicas de reconhecimento de padr?es utilizados em c?meras de padr?o RGB comuns, t?cnicas de estimativa de posi??o e orienta??o e algoritmos de controle inteligentes, que possuem baixo custo computacional, aplic?veis a rob?s com restri??es de movimenta??o.<br>The ability to follow or move along with a specified moving person or object, is a necessary skill in several autonomous agents. Such agents are widely used to perform various tasks in everyday life, and they can be applied either in everyday tasks, such as in supermarket carts or cleaning environments, as well in high-risk tasks like large industries or autonomous cars. The idea presented here is to develop a target tracking and following method applicable to mobile wheeled land robots that have restrictions on their movement, which means that standard control techniques cannot always be applied. The work developed here also takes into account the use of a target detection technique that can be adapted to practically any type of target stipulated by the designer according to the needs of its application. The development of the proposed methods is accomplished by adding standard recognition techniques used in common RGB type cameras, position estimation and orientation techniques, and intelligent control algorithms, with a low computational cost, applicable to robots with movement restrictions.

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Book chapters on the topic "HAAR cascade algorithm"

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Malarmannan, A., S. Sushmitha, M. Sanjana, and M. Salai Sangavi. "Ocular handled virtual mouse using Haar cascade algorithm." In Hybrid and Advanced Technologies. CRC Press, 2025. https://doi.org/10.1201/9781003559139-34.

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S. S. Sunaina M, L. S. N. J. Manjusha P, and Kishore O. "Virtual Ornament Room Using Haar Cascade Algorithm During Pandemic." In Information and Communication Technology for Competitive Strategies (ICTCS 2021). Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0095-2_24.

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Gangopadhyay, Indrasom, Anulekha Chatterjee, and Indrajit Das. "Face Detection and Expression Recognition Using Haar Cascade Classifier and Fisherface Algorithm." In Recent Trends in Signal and Image Processing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6783-0_1.

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Sharma, Arpit, and N. Jayapandian. "Face Detection-Based Border Security System Using Haar-Cascade and LBPH Algorithm." In Proceedings of International Conference on Data Science and Applications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6631-6_3.

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Rai, Akshat Kumar, A. Akash, G. Kavyashree, and Thaseen Taj. "Attendance System Based on Face Recognition Using Haar Cascade and LBPH Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5936-3_2.

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Alankar, Bhavya, Mohammad Sharay Ammar, and Harleen Kaur. "Facial Emotion Detection Using Deep Learning and Haar Cascade Face Identification Algorithm." In Lecture Notes in Networks and Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0695-3_17.

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Lakshmi, D., R. Janaki, V. Subashini, K. Senthil Kumar, C. A. Catherine Aurelia, and S. T. Ananya. "Prediction of Age, Gender, and Ethnicity Using Haar Cascade Algorithm in Convolutional Neural Networks." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5881-8_17.

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Sangeetha, T., V. Miruthula, C. Kavimalar, and V. Aakash. "Face Mask Detection and Social Distancing Using Machine Learning with Haar Cascade Algorithm." In Advances in Intelligent Systems and Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5443-6_72.

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Tabrizi, Sahar S., Nuriye Sancar, and Gunay Sadikoğlu. "Detection of Eye Staring for Early Warning of Digital Eye Strain in VDT Users: An Analysis Using Haar Cascade Algorithm." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-72506-7_32.

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Sathya, R., D. Sai Surya Harsha, G. Pavan Sundar Reddy, and M. Gopala Krishna. "IoT-Based Driver Drowsiness Detection and Alerting System Using Haar Cascade and Eye Aspect Ratio Algorithms." In Integration of AI-Based Manufacturing and Industrial Engineering Systems with the Internet of Things. CRC Press, 2023. http://dx.doi.org/10.1201/9781003383505-17.

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Conference papers on the topic "HAAR cascade algorithm"

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J, Vijaya, Anurag Pratap Singh, Michel Ekka, Peethala Navya, and Subhali AR Otti. "Face Recognition System Using Haar Cascade Algorithm." In 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET). IEEE, 2024. http://dx.doi.org/10.1109/acroset62108.2024.10743750.

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Praneeth, D., T. Rekha Sree, V. Viswanatha, and M. Abhiram Sai. "AI Enabled Home Security System Using Object Detection and Face Recognition with Haar Cascade Algorithm." In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET). IEEE, 2024. https://doi.org/10.1109/icraset63057.2024.10895751.

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Chittibomma, Sukith Sai, Ravi Kishan Surapaneni, and Afraim Maruboina. "Facial Recognition System for Law Enforcement: An Integrated Approach Using Haar Cascade Classifier and LBPH Algorithm." In 2024 International Conference on Advancements in Power, Communication and Intelligent Systems (APCI). IEEE, 2024. http://dx.doi.org/10.1109/apci61480.2024.10616450.

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Shetty, Shravya, Thanu Kurian, Nithesh Pai, M. Prajna, B. Jaishma Kumari, and N. S. Krishnaraj Rao. "Virtual Trial System using Haar Cascades Classifier Algorithm." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724466.

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Sharmila, G., I. Varalakshmi, M. Kaviya, K. Neha, M. Juhe Sherin, and P. Suganya. "Attendance Management System using Haar Cascades and MTCNN Algorithm." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894173.

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Šarkoćević, Ivan, Vladimir Maksimović, Branimir Jakšić, Petar Spalević, and Đoko Banđur. "Performance Analysis of Haar Cascade-Based Face Detection in Multi-Face Images under Diverse Compression Algorithms." In Sinteza 2025. Singidunum University, 2025. https://doi.org/10.15308/sinteza-2025-164-171.

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Ali, Shahad Salh, Jamila Harbi Al' Ameri, and Thekra Abbas. "Face Detection Using Haar Cascade Algorithm." In 2022 Fifth College of Science International Conference of Recent Trends in Information Technology (CSCTIT). IEEE, 2022. http://dx.doi.org/10.1109/csctit56299.2022.10145680.

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Keerthi, Lagisetty, Poleboina Divya, and U. Sairam. "Student Attendance Recognition using the Haar Cascade Algorithm." In 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS). IEEE, 2023. http://dx.doi.org/10.1109/icscss57650.2023.10169646.

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Singh, Nongmeikapam Thoiba, Samridh Rana, Sonal Kumari, and Ritu. "Facial Emotion Detection Using Haar Cascade and CNN Algorithm." In 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT). IEEE, 2023. http://dx.doi.org/10.1109/iccpct58313.2023.10245125.

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Vara Prasad, K., D. Hemanth Sai Kumar, T. Mohith, Md Sameer, and K. Lohith. "Classroom Attendance Monitoring using Haar Cascade and KNN Algorithm." In 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2024. http://dx.doi.org/10.1109/idciot59759.2024.10467696.

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