Relevant bibliographies by topics / Viola-Jones method

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Viola-Jones method'

Author: Grafiati
Published: 28 May 2022
Last updated: 19 June 2025

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Journal articles on the topic "Viola-Jones method"

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Cherednyk, Oleksandr, and Elżbieta Miłosz. "Object recognition on video from camera to computer." Journal of Computer Sciences Institute 8 (November 30, 2018): 215–19. http://dx.doi.org/10.35784/jcsi.682.

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The goal is to determine the effectiveness of object detection in a video using the camera for the computer. In the course of work studied and described the main methods of recognition of objects in the image, namely the use of artificial neural networks and techniques of Viola-Jones. For the study, based on the method of Viola-Jones, implemented the application for object recognition in video, as this method is effective for solving this problem. With this application, a study was conducted to determine the effectiveness of the method of viola-Jones to detect objects in the video.
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Winarno, Edy, Wiwien Hadikurniawati, Ahmad Ainun Nirwanto, and Dahlan Abdullah. "Multi-View Faces Detection Using Viola-Jones Method." Journal of Physics: Conference Series 1114 (November 2018): 012068. http://dx.doi.org/10.1088/1742-6596/1114/1/012068.

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Abdulsamad, Taib Shamsadin, Mahmud Abdulla Mohammad, and Faraidoon Hassan Ahmad. "Adapting Viola-Jones Method for Online Hand / Glove Identification." UKH Journal of Science and Engineering 5, no. 1 (2021): 80–90. http://dx.doi.org/10.25079/ukhjse.v5n1y2021.pp80-90.

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This article proposes a method for hand identification, adapting the method of Viola-Jones for identifying two different objects. The main objective of this work is to solve the problems of hand identification. Thus, our approach based on learning for two objects as one package. Also, the proposed method folds into three parts; the first part is training for both objects, second detection of both objects, and third the identification step to identify if the hand is wearing a glove or not, then labeling each one with a suitable state. Moreover, to test our method, we have proposed a new dataset, which includes a variety of cases with different compositions of hand. As a result, 8 cases were used to test the method. The method was able to detect a human hand successfully. Additionally, it could identify whether the hand was or was not wearhing a glove. The accuracy of detecting a hand without a glove was about 63%, and the accuracy of detecting a hand with a glove on was about 61%. Even though the tests scored different accuracy, as a first step towards solving this problem, it is a big achievement to even reach this level of accuracy.
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Stevanović, Dušan. "OBJECT DETECTION USING VIOLA-JONES ALGORITHM." Knowledge International Journal 28, no. 4 (2018): 1349–54. http://dx.doi.org/10.35120/kij28041349d.

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In this paper it has been described and applied method for detecting face and face parts in images using the Viola-Jones algorithm. The work is based on Computer Vision Systems, artificial intelligence that deals with the recognition of two-dimensional or three-dimensional objects. When Cascade Object Detector script is trained, multimedia content is assigned for recognition. In this work the content will be in the form of an image, where the program will have the task of recognizing the objects in the images, separating the parts of the images in the head area, and on each discovered face, separately mark the area around the eyes, nose and mouth.Algorithm for detection and recognition is based on scanning and analyzing front part of human head. Common usage of face detection and recognition can be find in biometry, photography, on autofocus option which is implemented in professional photo cameras or on smiling detectors (Keller, 2007). Marketing is also popular field where face detection and recognition can be used. For example, web cameras that are implemented in TVs, can detect every face in near area. Calculating different type of algorithms and parameters, based on sex, age, ethnicity, system can play precisely segmented television commercials and campaigns. Example of that kind of systems is OptimEyes. (Strasburger, 2013)In other words, every algorithm that has as its main goal to detect and recognize face from image, should give as a feedback information, is there any face and if answer is positive, where is its location on image. In order to achieve acceptable performances, algorithm should minimize false recognitions. These are the cases when the algorithm ignores and does not recognize the real object from the image, and vice versa, when the wrong object is recognized as real. One of the algorithms that is frequently applied in this area of research is the Viola-Jones algorithm. This algorithm is functional in real time, meaning that besides detection, it is also possible to adjust the ability to monitor faces from video material.In this paper, the problem that will be analyzed is facial image detection. Man can do this task in a very simple way, but to do the same with a computer, it is necessary to have a range of precise and accurate information, formulas, methods and techniques. In order to maximize the precision of recognizing the face of the image using the Viola-Jones algorithm, it is desirable that the objects in the images are completely face-to-face with the image-taking device, which will be shown through experiments.
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Senjaya, Benny, Alexander A. S. Gunawan, and Jerry Pratama Hakim. "Pendeteksian Bagian Tubuh Manusia untuk Filter Pornografi dengan Metode Viola-Jones." ComTech: Computer, Mathematics and Engineering Applications 3, no. 1 (2012): 482. http://dx.doi.org/10.21512/comtech.v3i1.2447.

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Information Technology does help people to get information promptly anytime and anywhere. Unfortunately, the information gathered from the Internet does not always come out positive. Some information can be destructive, such as porn images. To mitigate this problem, the study aims to create a desktop application that could detect parts of human body which can be expanded in the future to become an image filter application for pornography. The detection methodology in this study is Viola-Jones method which provides a complete framework for extracting and recognizing image features. A combination of Viola-Jones method with Haar-like features, integral image, boosting algorithm, and cascade classifier provide a robust detector for the application. First, several parts of the human body are chosen to be detected as the data training using the Viola-Jones method. Then, another set of images (similar body parts but different images) are run through the application to be recognized. The result shows 86.25% of successful detection. The failures are identified and show that the inputted data are completely different with the data training.
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Raghuveera, T., S. Vidhushini, and M. Swathi. "Comparative Study of CAMSHIFT and RANSAC Methods for Face and Eye Tracking in Real-Time Video." International Journal of Intelligent Information Technologies 13, no. 2 (2017): 63–75. http://dx.doi.org/10.4018/ijiit.2017040104.

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Real-Time Facial and eye tracking is critical in applications like military surveillance, pervasive computing, Human Computer Interaction etc. In this work, face and eye tracking are implemented by using two well-known methods, CAMSHIFT and RANSAC. In our first approach, a frontal face detector is run on each frame of the video and the Viola-Jones face detector is used to detect the faces. CAMSHIFT Algorithm is used in the real- time tracking along with Haar-Like features that are used to localize and track eyes. In our second approach, the face is detected using Viola-Jones, whereas RANSAC is used to match the content of the subsequent frames. Adaptive Bilinear Filter is used to enhance quality of the input video. Then, we run the Viola-Jones face detector on each frame and apply both the algorithms. Finally, we use Kalman filter upon CAMSHIFT and RANSAC and compare with the preceding experiments. The comparisons are made for different real-time videos under heterogeneous environments through proposed performance measures, to identify the best-suited method for a given scenario.
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Asni b, Andi, and Tamara Octa Dana. "Identifikasi Wajah Dengan Segmentasi Warna Kulit Menggunakan Metode Viola Jones." Jurnal Teknik Elektro Uniba (JTE Uniba) 4, no. 1 (2019): 1–6. http://dx.doi.org/10.36277/jteuniba.v4i1.47.

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Abstract - Face detection (face detection) is one of the initial steps that is very important before the face recognition process (face recognition). Face detection is the detection of objects in the form of faces in which there are special features that represent the shape of faces in general. One method of face detection is the Viola Jones method. Viola Jones method is used to detect faces and skin color segmentation, test data processing using Matlab and capture on a Smartphone. The test is carried out at normal light intensity with a predetermined distance and face position. The results of this study indicate the level of accuracy of detection of face image variations in the position of face images facing forward (frontal), sideways left and right 45̊. But it has a weakness of this face detection system that is unable to determine faces in images that have faces that are not upright (tilted) or not frontal (facing sideways) at a 90̊ angle. Face position that is upright / not upright will determine the success of this face detection. The level of identification of the Viola Jones simulation was 100% with 4 images consisting of 3 boys and 1 girl.
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Sikarwar, Ranjana, and Priyanka Nema. "Localization of Facial Features Using Viola- Jones and Canny Method." IOSR Journal of Computer Engineering 19, no. 02 (2017): 16–21. http://dx.doi.org/10.9790/0661-1902021621.

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Arlazarov, V. V., Ju S. Voysyat, D. P. Matalov, D. P. Nikolaev, and S. A. Usilin. "Evolution of the Viola-Jones Object Detection Method: A Survey." Bulletin of the South Ural State University. Series "Mathematical Modelling, Programming and Computer Software" 14, no. 4 (2021): 52–23. http://dx.doi.org/10.14529/mmp210401.

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Dagher, Issam, and Hussein Al-Bazzaz. "Improving the Component-Based Face Recognition Using Enhanced Viola–Jones and Weighted Voting Technique." Modelling and Simulation in Engineering 2019 (April 3, 2019): 1–9. http://dx.doi.org/10.1155/2019/8234124.

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This paper enhances the recognition capabilities of the facial component-based techniques using the concepts of better Viola–Jones component detection and weighting facial components. Our method starts with enhanced Viola–Jones face component detection and cropping. The facial components are detected and cropped accurately during all pose-changing circumstances. The cropped components are represented by the histogram of oriented gradients (HOG). The weight of each component was determined using a validation process. Combining these weights was done by a simple voting technique. Three public databases were used: the AT&T database, the PUT database, and the AR database. Several improvements are observed using the weighted voting recognition method presented in this paper.
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Dissertations / Theses on the topic "Viola-Jones method"

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Bernátek, Pavel. "Vizualizace pulzu ve videozáznamu obličeje." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-242090.

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In the semestral thesis is given basic methods of non-contact measurement heart rate. There is explained Eulerian video magnification method deals with the visualization of the pulse in the videosequence of face. The semestral thesis describes algorithm Viola-Jones face detection in images and algorithm Kanade-Lucas-Tomasi for tracking faces in the videosequence. Part of the work includes design and realization of measurement. There is explained realization of the program and documented execution results, which are discussed. From the results it is designed to guide for optimal recording.
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Терьохін, А. А. "Аналіз та ідентифікація зображень, які містять складні текстурні патерни". Master's thesis, Сумський державний університет, 2020. https://essuir.sumdu.edu.ua/handle/123456789/81367.

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У 2001 році Віола і Джонс запропонували алгоритм для розпізнавання осіб, який став проривом в області розпізнавання осіб. Метод використовує технологію ковзного вікна. Тобто рамка, розміром, меншим, ніж вихідне зображення, рухається з деяким кроком по зображенню, і за допомогою каскаду слабких класифікаторів визначає, чи є в даному вікні особа. Метод змінного вікно ефективно використовується в різних завданнях комп'ютерного зору і розпізнавання об'єктів
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Kubínek, Jiří. "Detekce objektů v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236646.

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This work is dedicated to methods used for object detection in images. There is a summary of several approaches and algorithms to solve this matter, especially AdaBoost algorithm with its improvement, WaldBoost and several features used for object detection. Vital part of this work is dedicated to extending training datasets for classifier training and extending the current object detection framework with histogram of gradients features implementation. Integral part of this work is analysis of results by experiments evaluation.
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Šujan, Miroslav. "Detekce částí obličeje v termografickém spektru." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219293.

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Master´s thesis deals with current problems of face detection and its parts in the infrared thermographic spectrum. Most previously published literature deals with the detection in the visible spectrum, making the thermographic detection range an interesting alternative. The work deals with the processing of image signals, images and faces in thermographic spectrum, selected methods of face detection and its parts and also deals with practical system design for detecting facial parts in this spectrum and its subsequent testing.
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Al-Dahoud, A., and Hassan Ugail. "A method for location based search for enhancing facial feature design." 2016. http://hdl.handle.net/10454/9482.

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No<br>In this paper we present a new method for accurate real-time facial feature detection. Our method is based on local feature detection and enhancement. Previous work in this area, such as that of Viola and Jones, require looking at the face as a whole. Consequently, such approaches have increased chances of reporting negative hits. Furthermore, such algorithms require greater processing power and hence they are especially not attractive for real-time applications. Through our recent work, we have devised a method to identify the face from real-time images and divide it into regions of interest (ROI). Firstly, based on a face detection algorithm, we identify the face and divide it into four main regions. Then, we undertake a local search within those ROI, looking for specific facial features. This enables us to locate the desired facial features more efficiently and accurately. We have tested our approach using the Cohn-Kanade’s Extended Facial Expression (CK+) database. The results show that applying the ROI has a relatively low false positive rate as well as provides a marked gain in the overall computational efficiency. In particular, we show that our method has a 4-fold increase in accuracy when compared to existing algorithms for facial feature detection.
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Book chapters on the topic "Viola-Jones method"

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Satyanarayana, P., N. Jaya Devi, S. K. Sri Hasitha, and M. Sesha Sai. "An Enhanced Viola–Jones Face Detection Method with Skin Mapping & Segmentation." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7868-2_47.

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Usilin, Sergey A., Oleg A. Slavin, and Vladimir V. Arlazarov. "Memory Consumption and Computation Efficiency Improvements of Viola-Jones Object Detection Method for UAVs." In Pattern Recognition. ICPR International Workshops and Challenges. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68821-9_23.

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Phan, Ngoc Hoang, and Thi Thu Trang Bui. "Context-Aware Hand Pose Classifying Algorithm Based on Combination of Viola-Jones Method, Wavelet Transform, PCA and Neural Networks." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56357-2_5.

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Morales, Santiago, César Pedraza Bonilla, and Felix Vega. "HAAR Characteristics-Based Traffic Volume Method Measurement for Street Intersections." In Pattern Recognition Applications in Engineering. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1839-7.ch011.

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Traffic volume is an important measurement to design mobility strategies in cities such as traffic light configuration, civil engineering works, and others. This variable can be determined through different manual and automatic strategies. However, some street intersections, such as traffic circles, are difficult to determine their traffic volume and origin-destination matrices. In the case of manual strategies, it is difficult to count every single car in a mid to large-size traffic circle. On the other hand, automatic strategies can be difficult to develop because it is necessary to detect, track, and count vehicles that change position inside an intersection. This chapter presents a vehicle counting method to determine traffic volume and origin-destination matrix for traffic circle intersections using two main algorithms, Viola-Jones for detection and on-line boosting for tracking. The method is validated with an implementation applied to a top view video of a large-size traffic circle. The video is processed manually, and a comparison is presented.
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"Facial feature point location and tracking method based on improved Viola-Jones algorithm and Kalman filter prediction mechanism." In Environment, Energy and Applied Technology. CRC Press, 2015. http://dx.doi.org/10.1201/b18135-177.

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Conference papers on the topic "Viola-Jones method"

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Da'san, Mohammad, Amin Alqudah, and Olivier Debeir. "Face detection using Viola and Jones method and neural networks." In 2015 International Conference on Information and Communication Technology Research (ICTRC). IEEE, 2015. http://dx.doi.org/10.1109/ictrc.2015.7156416.

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Sheshkus, Alexander, Daniil Matalov, Vladimir V. Arlazarov, and Dmitry P. Nikolaev. "Viability of Viola-Jones method for the problem of image classification." In Eleventh International Conference on Machine Vision, edited by Dmitry P. Nikolaev, Petia Radeva, Antanas Verikas, and Jianhong Zhou. SPIE, 2019. http://dx.doi.org/10.1117/12.2522971.

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Putro, M. Dwisnanto, Teguh Bharata Adji, and Bondhan Winduratna. "Adult image classifiers based on face detection using Viola-Jones method." In 2015 1st International Conference on Wireless and Telematics (ICWT). IEEE, 2015. http://dx.doi.org/10.1109/icwt.2015.7449208.

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Usilin, Sergey A., Pavel V. Bezmaternykh, and Vladimir V. Arlazarov. "Fast approach for QR code localization on images using Viola-Jones method." In Twelfth International Conference on Machine Vision, edited by Wolfgang Osten and Dmitry P. Nikolaev. SPIE, 2020. http://dx.doi.org/10.1117/12.2559386.

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Yun, Liu, and Zhang Peng. "An Automatic Hand Gesture Recognition System Based on Viola-Jones Method and SVMs." In 2009 Second International Workshop on Computer Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/wcse.2009.769.

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Djamaluddin, Dewiani, Tantri Indrabulan, Andani, Indrabayu, and Sitti Wetenriajeng Sidehabi. "The simulation of vehicle counting system for traffic surveillance using Viola Jones method." In 2014 Makassar International Conference on Electrical Engineering and Informatics (MICEEI). IEEE, 2014. http://dx.doi.org/10.1109/miceei.2014.7067325.

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Андреянов, Н. В. "RECOGNITION OF OBJECTS OF INTEREST IN THE AIR USING THE VIOLA JONES METHOD." In САПР и моделирование в современной электронике. Брянский государственный технический университет, 2020. http://dx.doi.org/10.51932/9785907271739_67.

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Loresco, Pocholo James, Ira Valenzuela, Alvin Culaba, and Elmer Dadios. "Viola-Jones Method of Marker Detection for Scale-Invariant Calculation of Lettuce Leaf Area." In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM). IEEE, 2018. http://dx.doi.org/10.1109/hnicem.2018.8666244.

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Egorov, Aleksej D., Almaz F. Idiyatullin, and Artur D. Zakirov. "Comparison of the Parametrically Optimized Implementation of Viola–Jones Object Detection Method and MTCNN." In 2021 IV International Conference on Control in Technical Systems (CTS). IEEE, 2021. http://dx.doi.org/10.1109/cts53513.2021.9562926.

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Samir, Allach, Ben Ahmed Mohamed, and Boudhir Anouar Abdelhakim. "Detection of driver drowsiness based on the Viola & Jones method and logistic regression analysis." In the Mediterranean Symposium. ACM Press, 2017. http://dx.doi.org/10.1145/3175628.3175650.

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