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Face detection by surveillance camera Using Machine Learning Computer Science D

Face detection by surveillance camera Using Machine Learning Computer Science Department University of batna 2 (Mustapha Ben Boulaïd) By Djouama Ihcene Oulmi Saliha A dissertation submitted to the University of Batna 2 in accordance with the requirements of the degree of Master: Artificial Intelligence and Multimedia of Mathematics and Computer Science Faculty. Directed by Dr Larbi GUEZOULI September 2020 i Abstract Abstract Face detection is the trend field of researches in this last few years , and this ups to the need to it in this decade and the most use technology ; where we can find it in different applications (examples: Snapchat Instagram face filters ), in different area to ensure the security in airports, train stations, security camera in homes and shops...etc , and this is what make it important and motivates the researchers to achieve remarkable works with high performances and guarantee real time efficiency. Which make it interesting subject to do it in our project to detect faces in real time in video. So as a first step we select four known methods that achieve state of the art in detecting faces ,by doing a comparative study between them ,then we are going to select the best one that respond to all the conditions and the requirements needed to achieve real time at least 20 frame per second ,and high accuracy with our dataset that we built it for surveillance scenarios. Keywords: Security camera, detecting faces, Faceboxes, Deep Learning, Cnn, Real time, machine learning. ii Abstract Résumé La détection des visages est le domaine de recherche de tendance de ces dernières années, et ceci dépend à des besoins dans cette décennie et la technologie la plus utilisée; où on peut le trouver dans différentes applications (exemples: filtres de visage Instagram, Snapchat), dans différents domaines pour assurer la sécurité dans les aéroports, les gares, la caméra de sécurité dans les maisons et les magasins ... etc, et c’est ce qui le rend important et motive les chercheurs pour réaliser des travaux remarquables avec des performances élevées et garantir une efficacité en temps réel dans un video. Ce qui rend le sujet intéressant de le faire dans notre projet de détection de visages en temps réel. Donc, dans un premier temps, nous sélectionnons quatre méthodes connues qui atteignent l’état de l’art dans la détection des visages, en faisant une étude comparative entre ces methods, puis nous allons sélectionner la meilleure qui répond à toutes les conditions et les exigences nécessaires pour atteindre le temps réel à au moins 20 images par seconde, et une grande précision avec notre base de données que nous l’avons construit pour les scénarios de surveillance. Mots clés: Caméra de sécurité, détection des visages , Faceboxes, Apprentissage profond, Cnn, Temps réel, Apprentissage Automatique. iii Abstract ملخص إن الكشف عن الوجوه هو من مجاԲԽت البحث العلمي اԲԽٔكثر استكشافا في السنوات القليلة الماضية ، وهذا يرجع إلى الحاجة إليه في هذا العقد و هي من التكنولوجيا اԲԽٔكثر استخدامًا ؛ حيث يمكن أن نجدها في تطبيقات مختلفة )أمثلة ، فلتر اԲԽنستقرام و سناب شات( ،وفي مناطق مختلفة لضمان اԲԽٔمن في المطارات ومحطات القطارات وكاميرات اԲԽٔمن في المنازل والمتاجر ... إلخ ، وهذا ما يجعلها مهمة ومحفزة للباحثون ԲԽٕنجاز أعمال رائعة بأداء عالٍ وضمان كفاءة الوقت الحقيقي. مما يجعل اԲԽٔمر مثيرًا لՄՏهتمام للقيام به في مشروعنا للكشف عن الوجوه في الوقت الفعلي في الفيديو. لذلك كخطوة أولى نختار أربع طرق معروفة في الكشف عن الوجوه وحققوا نتائج مبهرة في مختلف الشروط ،ثم من خՄՏل إجراء دراسة مقارنة بينها ، نختار أفضل طريقة تستجيب لجميع الشروط والمتطلبات الՄՏزمة ԲԽحراز الكشف في الوقت الحقيقي في وقت ԲԽ يقل صورة في الثانية ، ودقة عالية مع مجموعة البيانات التي أنشأناها لسيناريوهات المراقبة.20عن ، الوقتCnn ،، التعلم العميقFaceboxes ،الكلمات المفتاحية: كاميرات المراقبة ،الكشف عن الوجوه الحقيقي ، التعلم اԲԽٓلي. iv Dedication To the pure soul, to the flower of my life, to you my mother in heaven. To my hero, to my source of motivation, to you my father. v Acknowledgements Foremost, i have to thank Allah to give me the power and patience to complete our thesis in these unsuspected situations of Corona Virus. I would like to express my sincere gratitude to our supervisor Dr LARBI GUEZOULI for his guidance that helped me during this research ,without his assistance , corrections , planning and dedicated involvement in every step through the process, this work would have never been accomplished. My sincere thanks also goes to all my teachers during all this years, without them i am not in this level of knowledge. Special thanks to my friends and my colleague in this thesis for their motivation and support during this work , to my two best friends who always been there for me , to my friend in Mostaganem who helped me to see this project easy by giving me tips. Last but not the least, i would like to thank my family, brothers and my dearest sister , none of this would happened without their trust on me ,their pushing and encouraging me spiritually ,most importantly to my father DJOUAMA ABDELAZIZ who did everything he could to offer me all what i need, and to make me where i am now by his advice , constant source of support ,and his certitude that i can do anything to be successful during all my years of study. Thanks to my self to not giving up. vi Acknowledgements First and foremost, I’m deeply grateful and thankful to ALLAH for giving us the strength, ability , knowledge to achieve this work. My gratitude knows no bounds to my colleague DJOUAMA IHCENE which really the most one supporting and encouraging me in my hard and expecting time this year. Best wishes for her. Moreover, I would like to express thanks for my dad and mom,my children DJANNA and MOUATEZ, my sister and brothers and my husband. Finally, a special thank to our supervisor Dr LARBI GUEZOULI. vii Author’s declaration W e declare that the work in this dissertation was carried out in accordance with the requirements of the University’s Regulations and Code of Prac- tice for Research Degree Programs, the work is the candidate’s own work. Work done in collaboration with, or with the assistance of, others, is indicated as such Email : SIGNED: .................................................... DATE: .......................................... viii Table of Contents Page List of Tables xiii List of Figures xiv 1 Introduction 1 1.1 Problems and objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Thesis plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Lexicon of used expressions 4 2.1 Video surveillance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Face detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3 Convolutional neural network (CNN) . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3.1 Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3.2 Convolution Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3.3 Strides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3.4 Padding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3.5 Non Linearity (ReLU) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3.6 Pooling Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . uploads/Science et Technologie/ face-detection-by-surveillance-camera.pdf