Relevant bibliographies by topics / Viola-Jones algorithm

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  2. Dissertations / Theses
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Academic literature on the topic 'Viola-Jones algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Viola-Jones algorithm"

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Stevanović, Dušan. "OBJECT DETECTION USING VIOLA-JONES ALGORITHM." Knowledge International Journal 28, no. 4 (December 10, 2018): 1349–54. http://dx.doi.org/10.35120/kij28041349d.

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In this paper it has been described and applied method for detecting face and face parts in images using the Viola-Jones algorithm. The work is based on Computer Vision Systems, artificial intelligence that deals with the recognition of two-dimensional or three-dimensional objects. When Cascade Object Detector script is trained, multimedia content is assigned for recognition. In this work the content will be in the form of an image, where the program will have the task of recognizing the objects in the images, separating the parts of the images in the head area, and on each discovered face, separately mark the area around the eyes, nose and mouth.Algorithm for detection and recognition is based on scanning and analyzing front part of human head. Common usage of face detection and recognition can be find in biometry, photography, on autofocus option which is implemented in professional photo cameras or on smiling detectors (Keller, 2007). Marketing is also popular field where face detection and recognition can be used. For example, web cameras that are implemented in TVs, can detect every face in near area. Calculating different type of algorithms and parameters, based on sex, age, ethnicity, system can play precisely segmented television commercials and campaigns. Example of that kind of systems is OptimEyes. (Strasburger, 2013)In other words, every algorithm that has as its main goal to detect and recognize face from image, should give as a feedback information, is there any face and if answer is positive, where is its location on image. In order to achieve acceptable performances, algorithm should minimize false recognitions. These are the cases when the algorithm ignores and does not recognize the real object from the image, and vice versa, when the wrong object is recognized as real. One of the algorithms that is frequently applied in this area of research is the Viola-Jones algorithm. This algorithm is functional in real time, meaning that besides detection, it is also possible to adjust the ability to monitor faces from video material.In this paper, the problem that will be analyzed is facial image detection. Man can do this task in a very simple way, but to do the same with a computer, it is necessary to have a range of precise and accurate information, formulas, methods and techniques. In order to maximize the precision of recognizing the face of the image using the Viola-Jones algorithm, it is desirable that the objects in the images are completely face-to-face with the image-taking device, which will be shown through experiments.
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Andryani, Nur Afny C. "Study of Viola Jones Face Detection on Color Image based on Skin Pigmentation Level." Jurnal Elektro dan Mesin Terapan 1, no. 1 (May 2015): 44–52. http://dx.doi.org/10.35143/elementer.v1i1.16.

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Automatic face detection has been very complex and challenging research topic due to the complexity of faces’ characteristics that is not rigid object. There have been many works on proposing robust algorithm on image detection. Many researcher use Viola Jones algorithm as their initial point and benchmark. The Viola-Jones face detection itself is the most popular and recent applicable algorithm that has been developed since 2004 by Paul Jones from Microsoft R&D and its co-inventor, Michael J. Jones from Mitsubishi R&D. Many previous works present the study on the Viola Jones algorithm subject to frontal face with no consideration on the skin pigmentation level. This paper presents study on The Viola Jones performance on color image that consider skin pigmentation level. To indicate the skin pigmentation level, the L* element on CIELAB color space is used. The skin pigmentation level is clustered into dark skin, brown skin and fair skin. The simulation result show that the Viola Jones performance tends to decrease when the skin pigmentation getting high (dark skin). Some hypotheses test had been done to support the claim.
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Dherya Bengani and Prof. Vasudha Bah. "Face Detection Using Viola Jones Algorithm." November 2020 6, no. 11 (November 23, 2020): 131–34. http://dx.doi.org/10.46501/ijmtst061124.

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Face detection is one of the most widely researched topics in recent times and is at the helm of the computer vision technology. This paper aims to review and study in detail the implementation of Viola Jones algorithm to detect faces in Realtime. Viola Jones algorithm is reviewed first followed by its main steps which include Haar features, integral image and cascading classifiers.
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Крысин, И. А., and К. А. Гаврилов. "Study of neural network approach application in face detection problem." Informacionno-technologicheskij vestnik, no. 4(30) (December 15, 2021): 111–17. http://dx.doi.org/10.21499/2409-1650-30-4-111-117.

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В статье приводится исследование применения сверточных нейронных сетей с разными структурами при решении задачи обнаружения лиц. Приводятся тесты и сравнение эффективности в сравнении с алгоритмом Viola-Jones по оценкам точности и скорости обработки. Проверяется точность при обнаружении лиц с очками. Результаты доказали возможность применения нейросетевого подхода. В статье описываются возможные варианты применения нейронных сетей в гибриде с алгоритмом Viola-Jones для более эффективного решения. The article brings study of convolutional neural network with various structures application in face detection problem. Tests and effectiveness comparison to Viola-Jones algorithm are carried out in terms of accuracy and speed of processing. Accuracy on face detection with glasses is tested. Results proved the ability of neural network approach application. The article describes possible options of using neural networks with Viola-Jones algorithm to improve performance on the solution.
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Mohamed Hatim, Shahirah. "Drowsy Driver Detection Using Viola-Jones Algorithm." Mathematical Sciences and Informatics Journal 2, no. 2 (November 30, 2021): 51–56. http://dx.doi.org/10.24191/mij.v2i2.13926.

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Drowsy driving is one of the factors that lead to road accidents which can cause dead. This is because driver does not able to give fully attention while driving. There are many factors that lead to driver drowsiness such as driving for a long time, do not have enough sleep and shift work. Thus, this research is proposed to develop a system to detect and alert drowsy driver by using Viola-Jones algorithm. Blinking rate is used as the indicator to determine either the driver is in drowsy or awake state. Viola-Jones algorithm is used to detect driver’s face and eyes in real time. Haar cascade classifier for frontal face and glasses eyes are used to train the system to detect driver’s face and eyes. In order to calculate eye blink, Eye Aspect Ratio (EAR) calculation is used to calculate and estimate of the eye-opening state in this system. The results of testing showed that the system with the Viola-Jones algorithm and Haar cascade classifier able to detect eyes blinking rate at the high accuracy percentages.
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Kirana, Chandra, and Burham Isnanto. "Face Identification For Presence Applications Using Violajones and Eigenface Algorithm." Jurnal Sisfokom (Sistem Informasi dan Komputer) 5, no. 2 (October 1, 2016): 7. http://dx.doi.org/10.32736/sisfokom.v5i2.189.

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Sistem presensi merupakan hal yang sangat penting di dalam suatu lingkup kerja, seperti perkantoran, sekolah, maupun universitas. Sistem presensi saat ini telah berkembang dalam berbagai jenis, diantaranya sistem presensi secara manual, barcode, sidik jari, iris mata dan pengenalan wajah. Saat ini telah rata-rata perusahaan telah menggunakan sistem presensi fingerprint. Sistem presensi menggunakan sidik jari ini masih memiliki kekurangan dan kelemahan yang disebabkan human error, seperti scansidik jari sulit diterima. Hal ini bisa dikarenakan kondisi jari yang tidak normal, seperti basah, kotor, terlalu kering, maupun ujung jari terkelupas. Dengan adanya perkembangan teknologi mobile saat ini, maka diusulkan sebuah sistem presensi berbasis wajah yang akan dibangun dengan menggunakan metode Viola–Jones dan Eigenface pada smartphone. Metode Viola - Jones merupakan algoritma yang paling banyak digunakan untuk mendeteksi wajah dikarenakan metode Viola-Jones mampu mendeteksi secara real-time, cepat, efisien, dan juga mempunyai keakuratan yang sangat tinggi dalam mendeteksi wajah. Metode Viola-Jones ini menggabungkan empat kunci utama yaitu: Haar Like Feature, Integral Image, AdaBoost Learning ,dan Cascade Classifier. Algoritma Eigenface digunakan untuk melakukan identifikasi citra wajah yang terdeteksi dari suatu citra wajah dengan menggunakan Principal Component Analysis (PCA). Setelah dilakukan pengujian, aplikasi presensi wajah yang diusulkan mampu menghasilkan tingkat akurasi sebesar 90,90%.
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AZMI, FADHILLAH, Amir Saleh, and N. P. Dharshinni. "Face Identification on Login Security Using Algorithm Combination of Viola-Jones and Cosine Similarity." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 4, no. 1 (July 20, 2020): 203–11. http://dx.doi.org/10.31289/jite.v4i1.3885.

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Data security by using an alphanumeric combination password is no longer used, so it needs to be added security that is difficult to be manipulated by certain people. One type of security is the type of biometrics technology using face recognition which has different characteristics by combining the Viola-Jones algorithm to detect facial features, GLCM (Gray Level Co-occurrence Matrix) for extracting the texture characteristics of an image, and Cosine Similarity for the measurement of the proximity of the data (image matching). The image will be detected using the Viola-Jones algorithm to get face, eyes, nose, and mouth. The image detection results will be calculated the value of the texture characteristics with the GLCM (Gray Level Cooccurrence Matrix) algorithm. Image matching using cosine similarity will determine or match the data stored in the database with new image input until identification results are obtained. The results obtained in this study get the level of accuracy of the identification of the three algorithms by 77.20% with the amount of data that was correctly identified as many as 386 out of 500 images.Keywords: Security, face recognition, Viola-Jones, Cosine Similarity.
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Dheyaa Ismael, Khansaa, and Stanciu Irina. "Face recognition using viola-jones depending on python." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (December 1, 2020): 1513. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1513-1521.

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<p>In this paper, the proposed software system based on face recognition the proposed system can be implemented in the smart building or any VIP building need security interring in general, The human face will be recognized from a stream of pictures or video feed, this technology recognizes the person according to the specific algorithm, the algorithm that employed in this paper is the Viola–Jones object detection framework by using Python. The task of the proposed facial recognition system consists of two steps, the first one was detected the human face from live video using the webcamera in the computer, and the second step recognizes if this face allowed to enter the building or not by comparing it with the existing database, the two steps depending on the OpenCV python by importing cv2 method for detecting the human face, the frames can be read or written to file with the cv2.imread and cv2.imwrite functions respectively Finally, this proposed software system can be used to control access in smart buildings as a rule and the advancement of techniques connected around there, Providing a security system is one of the most important features must be achieved in the smart buildings, this proposed system can be used as an application in a smart building as a security system. Face recognition is one of the most important applications using today for practical facial recognition, The proposed software system, depending on using OpenCV (Open Source Computer Vision) is a popular computer vision library, in 1999 this library started by Intel. The platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. OpenCV 2.3.1 now comes with a programming interface to C, C++, Python, and Android. OpenCV library of python, the three algorithms that will be used in this proposed system. The currently available algorithms are:</p><p>Eigenfaces → createEigenFaceRecognizer()</p><p>Fisherfaces → createFisherFaceRecognizer()</p><p>Local Binary Patterns Histograms → createLBPHFaceRecognizer()</p>Finally the proposed system provide entering to the building just for the authorized person according to face recognition algorithem.<p> </p>
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I., Ahmed, and Yasser A. "Face Gestures Detection using Improved Viola-Jones Algorithm." International Journal of Computer Applications 182, no. 47 (April 11, 2019): 38–41. http://dx.doi.org/10.5120/ijca2019918719.

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

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

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Cöster, Jonatan, and Michael Ohlsson. "Human Attention : The possibility of measuring human attention using OpenCV and the Viola-Jones face detection algorithm." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166584.

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The question of whether an audience is focused and attentive can be of great importance. Research shows that a main concern during lectures is the varying level of attention from the students. Getting real time feedback on the students attention could give the lecturer an insight into what can be improved in terms of the material being presented. One potential way to get this feedback is to have a face detection algorithm to measure when someone is paying attention. The objective of the study is to investigate if it is possible to measure a person’s attention in a controlled environment using the OpenCV programming library and the Viola-Jones algorithm. In order to measure if someone was paying attention, a definition of attention was required. It is obvious to humans when someone is paying attention. However, this is not the case for a computer. A data set consisting of pictures of attentive and inattentive subjects was used to evaluate whether the software could be used to measure attention. The results of the study showed that OpenCV had an almost perfect detection rate with few false positives. The conclusion is therefore that the OpenCV programming library could be used to measure attention in a controlled environment. However, due to the limited scope of the study, further investigations are required in order to use it in a real-world application.
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Марченко, Ігор Олександрович, Игорь Александрович Марченко, Ihor Oleksandrovych Marchenko, Сергій Олександрович Петров, Сергей Александрович Петров, and Serhii Oleksandrovych Petrov. "Модифікація алгоритму Віоли-Джонса шляхом аналізу регіонів з визначеною текстурою." Thesis, Cумський державний університет, 2016. http://essuir.sumdu.edu.ua/handle/123456789/47008.

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Відомим недоліком алгоритму Віоли-Джонса є послідовна обробка всього зображення, незважаючи на особливості об‘єкту, що локалізується або розпізнається [1]. Це особливо уповільнює алгоритм у випадках складного фону або малого розміру об‘єкту відносно всього зображення.
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RODRIGUES, Matheus Bezerra Estrela. "Estudo da aplicação do algoritmo Viola-Jones à detecção de pneus com vistas ao reconhecimento de automóveis." Universidade Federal de Campina Grande, 2012. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1861.

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Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-10-01T15:06:04Z No. of bitstreams: 1 MATHEUS BEZERRA ESTRELA RODRIGUES - DISSERTAÇÃO PPGCC 2012..pdf: 7068761 bytes, checksum: 4b1283a1da5ca466fcf0357c33091a30 (MD5)
Made available in DSpace on 2018-10-01T15:06:04Z (GMT). No. of bitstreams: 1 MATHEUS BEZERRA ESTRELA RODRIGUES - DISSERTAÇÃO PPGCC 2012..pdf: 7068761 bytes, checksum: 4b1283a1da5ca466fcf0357c33091a30 (MD5) Previous issue date: 2012-02-29
Impulsionado pelo crescimento no uso de vigilância eletrônica, essa pesquisa introduz o uso de uma técnica que demonstra eficiência no reconhecimento de faces em imagens, alterando o objeto de busca para pneus de veículos, visando o reconhecimento da presença do veículo na cena. A técnica aplicada para o reconhecimento é o algoritmo Viola-Jones. Essa técnica é dividida em dois momentos: o treinamento e a detecção. Na primeira etapa, vários treinamentos são executados, usando aproximadamente 7000 imagens diferentes. Para a etapa final, um detector de faces foi adaptado para reconhecer pneus, utilizando o treinamento da etapa anterior, e sua eficiência em reconhecer os pneus foi comparável à eficiência do detector de faces que usa treinamento de referência da biblioteca em software que é referência nesta área, OpenCV. O detector desenvolvido apresentou taxa de reconhecimento de 77%, quando o reconhecimento de faces obteve 80%. A taxa de falsos negativos também foi próxima, apresentando o detector de pneus 2% e o de faces 1%.
Motivated by the growing use of electronic surveillance, this research introduces the use of the Viola-Jones algorithm, which is known to be efficient in recognition of human faces in images, changing the object to be recognized to vehicle tires, aiming to detect vehicles in a scene. This approach divides the process in two steps: training and detection. Training was done using around 7000 different images of vehicles. For the detection step, work was done to adapt a face detector to detect vehicles tires. The tire detector was compared to a face detector that used a reference training for faces from OpenCV library. The tire detector showed 77% efficiency, whereas the face detector showed 80%. False negative numbers also showed similar closeness, as 2% for the tire detector and 1% for the reference face detector.
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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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Krolikowski, Martin. "Automatické detekce obličeje a jeho jednotlivých částí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217286.

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The master thesis presents an overview of face detection task in color, static images. Face detection term is posed in the context of various branches. Main concepts of face detection and also their relationships are described. Individual approaches are divided into groups and then define in turn. In the thesis is in detail described algorithm AdaBoost, which is selected on the basis of its properties. Especially speed of computation and good detection results are key features. In the scope of this work Viola-Jones detector was implemented. This detector was trained with face pictures from public accessible database. Combination of Viola-Jones detector with simple color detector is described. In the thesis is also presented experiment approach to facial features detection.
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Pathare, Sneha P. "Detection of black-backed jackal in still images." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/97023.

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Thesis (MSc)--Stellenbosch University, 2015.
ENGLISH ABSTRACT: In South Africa, black-back jackal (BBJ) predation of sheep causes heavy losses to sheep farmers. Different control measures such as shooting, gin-traps and poisoning have been used to control the jackal population; however, these techniques also kill many harmless animals, as they fail to differentiate between BBJ and harmless animals. In this project, a system is implemented to detect black-backed jackal faces in images. The system was implemented using the Viola-Jones object detection algorithm. This algorithm was originally developed to detect human faces, but can also be used to detect a variety of other objects. The three important key features of the Viola-Jones algorithm are the representation of an image as a so-called ”integral image”, the use of the Adaboost boosting algorithm for feature selection, and the use of a cascade of classifiers to reduce false alarms. In this project, Python code has been developed to extract the Haar-features from BBJ images by acting as a classifier to distinguish between a BBJ and the background. Furthermore, the feature selection is done using the Asymboost instead of the Adaboost algorithm so as to achieve a high detection rate and low false positive rate. A cascade of strong classifiers is trained using a cascade learning algorithm. The inclusion of a special fifth feature Haar feature, adapted to the relative spacing of the jackal’s eyes, improves accuracy further. The final system detects 78% of the jackal faces, while only 0.006% of other image frames are wrongly identified as faces.
AFRIKAANSE OPSOMMING: Swartrugjakkalse veroorsaak swaar vee-verliese in Suid Afrika. Teenmaatreels soos jag, slagysters en vergiftiging word algemeen gebruik, maar is nie selektief genoeg nie en dood dus ook vele nie-teiken spesies. In hierdie projek is ’n stelsel ontwikkel om swartrugjakkals gesigte te vind op statiese beelde. Die Viola-Jones deteksie algoritme, aanvanklik ontwikkel vir die deteksie van mens-gesigte, is hiervoor gebruik. Drie sleutel-aspekte van hierdie algoritme is die voorstelling van ’n beeld deur middel van ’n sogenaamde integraalbeeld, die gebruik van die ”Adaboost” algoritme om gepaste kenmerke te selekteer, en die gebruik van ’n kaskade van klassifiseerders om vals-alarm tempos te verlaag. In hierdie projek is Python kode ontwikkel om die nuttigste ”Haar”-kenmerke vir die deteksie van dié jakkalse te onttrek. Eksperimente is gedoen om die nuttigheid van die ”Asymboost” algoritme met die van die ”Adaboost” algoritme te kontrasteer. ’n Kaskade van klassifiseerders is vir beide van hierdie tegnieke afgerig en vergelyk. Die resultate toon dat die kenmerke wat die ”Asymboost” algoritme oplewer, tot laer vals-alarm tempos lei. Die byvoeging van ’n spesiale vyfde tipe Haar-kenmerk, wat aangepas is by die relatiewe spasieëring van die jakkals se oë, verhoog die akkuraatheid verder. Die uiteindelike stelsel vind 78% van die gesigte terwyl slegs 0.006% ander beeld-raampies verkeerdelik as gesigte geklassifiseer word.
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Brunclík, Robert. "Automatická regulace velikosti písma podle vzdálenosti čtenáře." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-241995.

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The thesis deals with automatic control the font size by the distance from the reader. It includes theoretical acquaintance with the face detection and subsequent tracking of the detected area during the scene. Furthermore, there is a comparison of the tracking algorithms. Then the calculation of distance is decribed. It is based on the user’s calibration and based on the outcome occurs the font size is automatically corrected. There is also a description of a separate application Automatical controller of the text size, with the recommended settings of the program.
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Тарановський, Антон Володимирович, Антон Владимирович Тарановский, Anton Volodymyrovych Taranovskyi, Сергій Олександрович Петров, Сергей Александрович Петров, and Serhii Oleksandrovych Petrov. "Визначення оптимальних параметрів вхідного зображення на характеристики розпізнавання з використанням алгоритму Віола-Джонса." Thesis, Видавництво СумДУ, 2013. http://essuir.sumdu.edu.ua/handle/123456789/42602.

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Об’єктом аналізу алгоритмів, що реалізуються в системах комп’ютерного зору та працюють у рамках вирішення питання детекції та розпізнавання образів, є відео- та фотоматеріали, що є результатом спостереження фото- та відеокамер. Характеристики вхідних даних різняться в залежності від технічних можливостей камер.
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Марченко, Ігор Олександрович, Сергій Олександрович Петров, Сергей Александрович Петров, Serhii Oleksandrovych Petrov, Игорь Александрович Марченко, and Ihor Oleksandrovych Marchenko. "Підвищення якості розпізнавання алгоритму Віоли-Джонса шляхом попередньої обробки зображень." Thesis, Видавництво ПНПУ ім. К.Д. Ушинського, 2015. http://essuir.sumdu.edu.ua/handle/123456789/42803.

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Розглянуто можливі шляхи оптимізації алгоритму Віоли-Джонса без модифікації базового підходу. З метою підвищення результатів розпізнавання запропоновано проводити попередню обробку вхідного зображення з використанням эфектів, таких як яскравість, контраст. В результаті якість розпізнавання підвищилась на 37.39%.
Рассмотрены возможные оптимизации алгоритма Виолы-Джонса без модификации базового алгоритма. С целью повышения качества распознавания предложено выполнять предварительную обработку изображения с помощью фильтров, таких как, яркость, контраст. В результате качество распознавания повысилось на 37,39%.
The paper showed improving of the detection rate without modifying the base algorithm. Detection rate improved by decreasing defects on the image. Changing brightness and contrast is one way to modify an image. The detection rate increased by 37.39%.
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Chmelíková, Lucie. "Bezkontaktní měření tepové frekvence z obličeje." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-241972.

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This thesis deals with the study of contactless and noninvasive methods for estimation of heart rate. Contactless measurement is based on capturing person faces by video camera and from sequences of pictures are estimated values of the heart rate. The theoretical part describes heart rate and methods that are being used to estimate heart rate from color changes in the face. It also contains testing of tracking algorithms. Practical part deals with user interface of program for contactless measurement of heart rate and its software solution. Thesis also contains statistical evaluation of program functionality.
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Book chapters on the topic "Viola-Jones algorithm"

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Anitha, J., G. Mani, and K. Venkata Rao. "Driver Drowsiness Detection Using Viola Jones Algorithm." In Smart Intelligent Computing and Applications, 583–92. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9282-5_55.

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García García, Yainet, Reinier Rodríguez Guillén, Y. García, and Alberto Taboada-Crispi. "Fast Optic Disc Localization Using Viola-Jones Algorithm." In IFMBE Proceedings, 435–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30648-9_56.

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Ghosh, Mayukh, Tathagata Sarkar, Darshan Chokhani, and Anilesh Dey. "Face Detection and Extraction Using Viola–Jones Algorithm." In Lecture Notes in Electrical Engineering, 93–107. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4035-3_9.

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Patil, Aseem. "Real Drowsiness Detection Using Viola–Jones Algorithm in Tensorflow." In Machine Learning and Information Processing, 317–29. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1884-3_30.

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Pande, Aditya, B. K. Rout, and Sangram K. Das. "Hyper-parameter Optimization on Viola Jones Algorithm for Gesture Recognition." In Lecture Notes in Electrical Engineering, 626–35. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4775-1_68.

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Pilania, Urmila, Manoj Kumar, and Gaganjot Kaur. "Region of Interest Using Viola-Jones Algorithm for Video Steganography." In Applied Computational Technologies, 405–15. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2719-5_38.

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Pertierra, Álvaro Pardo, Ana B. Gil González, Javier Teira Lafuente, and Ana de Luis Reboredo. "Communication Skills Personal Trainer Based on Viola-Jones Object Detection Algorithm." In Intelligent Data Engineering and Automated Learning – IDEAL 2018, 722–29. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03493-1_75.

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Basbrain, Arwa M., John Q. Gan, and Adrian Clark. "Accuracy Enhancement of the Viola-Jones Algorithm for Thermal Face Detection." In Intelligent Computing Methodologies, 71–82. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63315-2_7.

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Rani, Kodeti Haritha, and Midhun Chakkaravarthy. "Improving Accuracy in Facial Detection Using Viola-Jones Algorithm AdaBoost Training Method." In Intelligent Systems and Sustainable Computing, 127–37. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0011-2_12.

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Kumar, Raghavendra, Himanshu Rathore, Palak Agrawal, and Prakhar Gupta. "Drowsiness Detection Using Viola–Jones Object Detection Algorithm for Real-Time Data." In Advances in Intelligent Systems and Computing, 369–80. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0171-2_35.

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Conference papers on the topic "Viola-Jones algorithm"

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Dahirou, Zoubaydat, Mao Zheng, and Mao Yuxin. "Face Detection with Viola Jones Algorithm." In 2020 7th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2020. http://dx.doi.org/10.1109/icisce50968.2020.00130.

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Al-Tuwaijari, Jamal M., and Saja A. Shaker. "Face Detection System Based Viola-Jones Algorithm." In 2020 6th International Engineering Conference “Sustainable Technology and Development" (IEC). IEEE, 2020. http://dx.doi.org/10.1109/iec49899.2020.9122927.

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Vikram, K., and S. Padmavathi. "Facial parts detection using Viola Jones algorithm." In 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2017. http://dx.doi.org/10.1109/icaccs.2017.8014636.

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Nehru, Mangayarkarasi, and S. Padmavathi. "Illumination invariant face detection using viola jones algorithm." In 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2017. http://dx.doi.org/10.1109/icaccs.2017.8014571.

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Li, Qian, Usman Niaz, and Bernard Merialdo. "An improved algorithm on Viola-Jones object detector." In 2012 10th International Workshop on Content-Based Multimedia Indexing (CBMI). IEEE, 2012. http://dx.doi.org/10.1109/cbmi.2012.6269796.

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Jia, Haipeng, Yunquan Zhang, Weiyan Wang, and Jianliang Xu. "Accelerating Viola-Jones Facce Detection Algorithm on GPUs." In 2012 IEEE 14th Int'l Conf. on High Performance Computing and Communication (HPCC) & 2012 IEEE 9th Int'l Conf. on Embedded Software and Systems (ICESS). IEEE, 2012. http://dx.doi.org/10.1109/hpcc.2012.60.

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Cuevas, Jerome, Alvin Chua, Edwin Sybingco, and Elmi Abu Bakar. "Identification of river hydromorphological features using Viola-Jones Algorithm." In 2016 IEEE Region 10 Conference (TENCON). IEEE, 2016. http://dx.doi.org/10.1109/tencon.2016.7848439.

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Chaudhari, Monali Nitin, Mrinal Deshmukh, Gayatri Ramrakhiani, and Rakshita Parvatikar. "Face Detection Using Viola Jones Algorithm and Neural Networks." In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018. http://dx.doi.org/10.1109/iccubea.2018.8697768.

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Kirana, Kartika Candra, Slamet Wibawanto, and Heru Wahyu Herwanto. "Emotion Recognition using Fisher Face-based Viola-Jones Algorithm." In 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2018. http://dx.doi.org/10.1109/eecsi.2018.8752783.

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Penna, Mahaveer, Shivashankar, K. Tulasi, N. Reddy Vasavi, N. Swathi, and S. R. Swapna. "Stampede Monitoring and Alarm System using Viola-jones algorithm." In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2018. http://dx.doi.org/10.1109/rteict42901.2018.9012229.

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