Embedded Face Detection and Facial Expression Recognition

Face Detection has been applied in many fields such as surveillance, human machine interaction, entertainment and health care. Two main reasons for extensive attention on this typical research domain are: 1) a strong need for the face recognition system is obvious due to the widespread use of security, 2) face recognition is more user friendly and faster since it almost requests the users to do nothing. The system is based on ARM Cortex-A8 development board, including transplantation of Linux operating system, the development of drivers, detecting face by using face class Haar feature and Viola-Jones algorithm. In the paper, the face Detection system uses the AdaBoost algorithm to detect human face from the frame captured by the camera. The paper introduces the pros and cons between several popular images processing algorithm. Facial expression recognition system involves face detection and emotion feature interpretation, which consists of offline training and online test part. Active shape model (ASM) for facial feature node detection, optical flow for face tracking, support vector machine (SVM) for classification is applied in this research

Design Of Human Facial Feature Recognition System

Augmenting human computer interaction with automated analysis and synthesis of facial expressions is the goal towards which much research effort has been devoted to in the last few years. Facial feature recognition is one of the important aspects of natural human-machine interfaces; it has great applications such as in behavioral science, security systems and in clinical practice. Although humans recognize facial expressions virtually without effort or delay, reliable expression recognition by machine is still a challenging task. The face expression recognition problem is challenging because different individuals display the same expression differently. In this project we are trying to design a facial feature recognition system in real time using the concepts of Haar classifiers, contour concepts, template matching and studying some models related to it. We have tried to first extract face region from the video using above mentioned approach and had tried to extract some facial features and locate their position in the image

A real-time facial expression recognition system for online games

Multiplayer online games (MOGs) have become increasingly popular because of the opportunity they provide for collaboration, communication, and interaction. However, compared with ordinary human communication, MOG still has several limitations, especially in communication using facial expressions. Although detailed facial animation has already been achieved in a number of MOGs, players have to use text commands to control the expressions of avatars. In this paper, we propose an automatic expression recognition system that can be integrated into an MOG to control the facial expressions of avatars. To meet the specific requirements of such a system, a number of algorithms are studied, improved, and extended. In particular, Viola and Jones face-detection method is extended to detect small-scale key facial components; and fixed facial landmarks are used to reduce the computational load with little performance degradation in the recognition accuracy

Human Face Detection and Segmentation of Facial Feature Region

Human face, facial feature detection and Segmentation have attracted a lot of attention because of their wide applications. In computer-human interaction, face recognition, video surveillance, security system and so many application use automatic face detection. This paper is about a study of detecting human faces within images and segmenting the face into numbered regions which are the face-, mouth-, eyes- and nose regions respectively. For face detection we have used the Viola2013;Jones object detection framework. Sometime the VJOD make a false frame of object detection. Here trying to detect the problem of identification and improve the detection quality by changing the threshold value. It detect the frontal face of human which is 2D. From detected face image we separate the extracted part of face in a single image and Segment nose, eyes, lip and hole face portion by Discontinuous based Image Segmentation. The development and experiments demonstration of this research is done on MATLAB 2013. The learning behavior of the algorithm was tested on different face of human

Automatic age estimation system for face images

Humans are the most important tracking objects in surveillance systems. However, human tracking is not enough to provide the required information for personalized recognition. In this paper, we present a novel and reliable framework for automatic age estimation based on computer vision. It exploits global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. In addition, the proposed system can extract face aging features automatically in real-time. This means that the proposed system has more potential in applications compared to other semi-automatic systems. The results obtained from this novel approach could provide clearer insight for operators in the field of age estimation to develop real-world applications. © 2012 Lin et al