การตรวจจับใบหน้าด้วยวิธีการพื้นฐานของการจำลองรูปแบบ Haar-like

This article presents a review of papers and researches on face detection based-on Haar-like features. The Haar-like features for face detection is proposed by Viola and Jones since 2001. This technique consists of 3 steps: Integral Image, Adaboost and Cascade Classifier. Since the Haar-like features is the most effective application used to extract features from faces, there are many researches aiming to apply the Haar-like features to achieve higher performance. The article also focuses on research papers which extend or adapt formations of the Haar-like features to get higher speed and accuracy or to detect faces in poses other than frontal faces.บทความวิชาการฉบับนี้เป็นการทบทวนเอกสาร และงานวิจัยเกี่ยวกับการตรวจจับใบหน้าด้วยวิธีการพื้นฐานของการจำลองรูปแบบ Haar-like ตั้งแต่อดีตจนถึงปัจจุบัน วิธีการนี้ได้ถูกนำเสนอเป็นครั้งแรกโดย Viola-Jones ในปี 2001 วิธีการตรวจจับใบหน้าของ Viola-Jones ประกอบด้วย 3 ขั้นตอน คือ การคำนวณการจำลองรูปแบบ Haar-like  ด้วย Integral Image   การค้นหาการจำลองรูปแบบ Haar-like ด้วย Adaboost   และการรวมตัวจำแนกกลุ่มแบบต่อเรียง (Cascade Classifier) ซึ่งในการใช้การจำลองรูปแบบ Haar-like นั้นถือว่ามีความสำคัญต่อความแม่นยำที่สุดเพราะเป็นเครื่องมือที่ใช้ในการดึงลักษณะเด่นจากใบหน้า จึงมีงานวิจัยเป็นจำนวนมากที่มุ่งเน้นในการพัฒนาการจำลองรูปแบบ Haar-like ดังนั้น บทความฉบับนี้จึงทำการรวบรวมและสรุปงานวิจัยที่มุ่งเน้นในการเพิ่มหรือปรับปรุงรูปร่างของการจำลองรูปแบบ Haar-like ซึ่งเป็นผลให้เพิ่มความเร็วและความแม่นยำในการตรวจจับใบหน้า หรือเพิ่มความสามารถในการตรวจจับใบหน้าในมุมอื่น ๆ นอกเหนือจากใบหน้าตร

Face detection in image

V této práci je prezentován přehled metod detekce obličeje v obraze a jsou vysvětleny základní principy klasifikace obrazu a jeho částí. Klíčovou částí práce je představení detektoru Viola-Jones a popis jeho implementace v jazyce Matlab. Detektor Viola-Jones je v praxi nejpoužívanější metoda pro detekci obličeje v obraze, což bylo důvodem pro detailní rozbor metody a následnou realizaci. Detektor je popsán teoreticky, rozebrány jsou základní kroky algoritmu a je zdokumentován trénovací algoritmus. Na základě teoretického rozboru byl detektor implementován v jazyce Matlab. Vlastnosti detektoru byly objektivně vyhodnoceny a porovnány s dalšími dvěma implementacemi detektoru Viola-Jones.This paper presents an overview of face detection methods. Keywords and basic principles of classification of images and it’s parts are explained. Significant part of this paper is occupied with presentation of Viola-Jones detector and it’s implementation in Matlab. Detector Viola-Jones ranks among the most used methods for face detection in practice, which was the reason for detailed analysis and subsequent implementation. Detector is theoretically described, basic steps of algorithm and training algorithm are discussed. Based on theoretical analysis, detector is implemented in Matlab. Properties of implemented detector are objectively evaluated and compared with of two different implementations.

Multi-Sensory Emotion Recognition with Speech and Facial Expression

Emotion plays an important role in human beings’ daily lives. Understanding emotions and recognizing how to react to others’ feelings are fundamental to engaging in successful social interactions. Currently, emotion recognition is not only significant in human beings’ daily lives, but also a hot topic in academic research, as new techniques such as emotion recognition from speech context inspires us as to how emotions are related to the content we are uttering. The demand and importance of emotion recognition have highly increased in many applications in recent years, such as video games, human computer interaction, cognitive computing, and affective computing. Emotion recognition can be done from many sources including text, speech, hand, and body gesture as well as facial expression. Presently, most of the emotion recognition methods only use one of these sources. The emotion of human beings changes every second and using a single way to process the emotion recognition may not reflect the emotion correctly. This research is motivated by the desire to understand and evaluate human beings’ emotion from multiple ways such as speech and facial expressions. In this dissertation, multi-sensory emotion recognition has been exploited. The proposed framework can recognize emotion from speech, facial expression, and both of them. There are three important parts in the design of the system: the facial emotion recognizer, the speech emotion recognizer, and the information fusion. The information fusion part uses the results from the speech emotion recognition and facial emotion recognition. Then, a novel weighted method is used to integrate the results, and a final decision of the emotion is given after the fusion. The experiments show that with the weighted fusion methods, the accuracy can be improved to an average of 3.66% compared to fusion without adding weight. The improvement of the recognition rate can reach 18.27% and 5.66% compared to the speech emotion recognition and facial expression recognition, respectively. By improving the emotion recognition accuracy, the proposed multi-sensory emotion recognition system can help to improve the naturalness of human computer interaction

Multi-Sensory Emotion Recognition with Speech and Facial Expression

Emotion plays an important role in human beings’ daily lives. Understanding emotions and recognizing how to react to others’ feelings are fundamental to engaging in successful social interactions. Currently, emotion recognition is not only significant in human beings’ daily lives, but also a hot topic in academic research, as new techniques such as emotion recognition from speech context inspires us as to how emotions are related to the content we are uttering. The demand and importance of emotion recognition have highly increased in many applications in recent years, such as video games, human computer interaction, cognitive computing, and affective computing. Emotion recognition can be done from many sources including text, speech, hand, and body gesture as well as facial expression. Presently, most of the emotion recognition methods only use one of these sources. The emotion of human beings changes every second and using a single way to process the emotion recognition may not reflect the emotion correctly. This research is motivated by the desire to understand and evaluate human beings’ emotion from multiple ways such as speech and facial expressions. In this dissertation, multi-sensory emotion recognition has been exploited. The proposed framework can recognize emotion from speech, facial expression, and both of them. There are three important parts in the design of the system: the facial emotion recognizer, the speech emotion recognizer, and the information fusion. The information fusion part uses the results from the speech emotion recognition and facial emotion recognition. Then, a novel weighted method is used to integrate the results, and a final decision of the emotion is given after the fusion. The experiments show that with the weighted fusion methods, the accuracy can be improved to an average of 3.66% compared to fusion without adding weight. The improvement of the recognition rate can reach 18.27% and 5.66% compared to the speech emotion recognition and facial expression recognition, respectively. By improving the emotion recognition accuracy, the proposed multi-sensory emotion recognition system can help to improve the naturalness of human computer interaction

Improved facial feature fitting for model based coding and animation

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Estimation robuste et dynamique de la pose de la tête d'un conducteur en situation de simulation de conduite automobile par vision artificielle

La conduite automobile est une activité importante pour une grande proportion de la population. Des études épidémiologiques ont démontré que la conduite dans des contextes difficiles, comme franchir une intersection, céder le passage ou se joindre au trafic, pose un défi aux conducteurs âgés. Ces tâches cognitives impliquent toutes des comportements céphalo-oculaires complexes de même que des actions de recherche visuelle. L'objet de cette maitrise effectuée dans le cadre du réseau d'excellence Auto21, est de développer un outil permettant d'analyser le comportement céphalo-occulaire du conducteur en temps réel dans un environnement sécuritaire. Le système exploite la vision artificielle pour estimer en tout temps la pose (position et orientation) de la tête du sujet dans un simulateur de conduite automobile. Le conducteur dans le simulateur observe la route grâce à un écran de réalité virtuelle. Ce dernier est filmé par 3 caméras calibrées et synchronisées à 30 images par secondes. Le système calcule la pose de sa tête en temps réel en utilisant une méthode basée sur une détection de blobs combinée à une validation par matching stéréo. Pour estimer la pose de la tête, le système recherche la position des yeux et du nez dans chaque image et reconstruit un plan à partir de ces trois points par triangulation. Ce plan permet d'estimer la pose de la tête du conducteur. Cette maitrise a débuté en septembre 2007 et s'est terminée en mai 2009. Elle vise à poursuivre le travail qui avait été réalisé par Frederic Ntawiniga sur le même sujet. Ce travail a consisté en une étude des méthodes existantes pour détecter et suivre un visage en temps réel. Elle s'est poursuivie par une optimisation des conditions d'acquisition des images dans le système afin de faciliter les traitements subséquents, et s'est terminée par l'implémentation d'un nouvel algorithme visant à améliorer la précision et la robustesse de l'estimation de la pose de la tête du conducteur

Iskanje obrazov na osnovi barv s pomočjo statističnih metod razpoznavanja vzorcev

V zadnjem času postaja video nadzor vse pomembnejši in s tem tudi sistemi za iskanje in prepoznavo človeških obrazov na slikah. Zato se v magistrskem delu ukvarjam s problemom iskanja obrazov na slikah. Pri metodah za iskanje obrazov na podlagi barve smo velikokrat omejeni na človeške obraze samo določene polti, same metode pa so tudi zelo odvisne od osvetlitve. V magistrskem delu zato poskušam s pomočjo kromatičnega barvnega prostora odvisnost od osvetlitve zmanjšati. Preizkusil bom različne metode za barvno segmentacijo na osnovi parametričnega in neparametričnega modela. S pomočjo teh modelov bom poskušal modelirati kožno barvo pri različnih osvetlitvah in različnih kožnih polteh. Uspešnost metod bom primerjal z metodo, ki deluje v barvnem prostoru RGB na osnovi eksplicitno določenih mej. Za potrjevanje označenih kožnih regij bom uporabil metodo na osnovi videza, ki nam med vsemi metodami obljublja najboljše rezultate. Izdelal in preizkusil bom metodo BDF, ki na osnovi naučenega vzorca obraza in neobraza s pomočjo Bayesovega klasifikatorja najde frontalne obraze na sivinskih slikah. Glavna slabost metod na osnovi videza je njihova časovna zahtevnost, zato bom poskušal izdelati metodo, ki bo kombinirala pristop na osnovi barv in pristop na osnovi videza. S pomočjo tako izdelane metode bom poskušal doseči hitro in učinkovito iskanje frontalnih obrazov na barvnih slikah

A Probabilistic Framework for Perceptual Grouping of Features for Human Face Detection

Present approaches to human face detection have made several assumptions that restrict their ability to be extended to general imaging conditions. We identify that the key factor in a generic and robust system is that of exploitinga large amount of evidence, related and reinforced by model knowledge through a probabilistic framework. In this paper, we propose a face detection framework that groups image features into meaningful entities using perceptual organization, assigns probabilities to each of them, and reinforce these probabilities using Bayesian reasoning techniques. True hypotheses of faces will be reinforced to a high probability. The detection of faces under scale, orientation and viewpoint variations will be examined in a subsequent paper