Facial expression recognition for a sociable robot

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 53-54).In order to develop a sociable robot that can operate in the social environment of humans, we need to develop a robot system that can recognize the emotions of the people it interacts with and can respond to them accordingly. In this thesis, I present a facial expression system that recognizes the facial features of human subjects in an unsupervised manner and interprets the facial expressions of the individuals. The facial expression system is integrated with an existing emotional model for the expressive humanoid robot, Mertz.by Wing Hei Iris Tang.M.Eng

Cultural dialects of real and synthetic emotional facial expressions

In this article we discuss the aspects of designing facial expressions for virtual humans (VHs) with a specific culture. First we explore the notion of cultures and its relevance for applications with a VH. Then we give a general scheme of designing emotional facial expressions, and identify the stages where a human is involved, either as a real person with some specific role, or as a VH displaying facial expressions. We discuss how the display and the emotional meaning of facial expressions may be measured in objective ways, and how the culture of displayers and the judges may influence the process of analyzing human facial expressions and evaluating synthesized ones. We review psychological experiments on cross-cultural perception of emotional facial expressions. By identifying the culturally critical issues of data collection and interpretation with both real and VHs, we aim at providing a methodological reference and inspiration for further research

Spotting Agreement and Disagreement: A Survey of Nonverbal Audiovisual Cues and Tools

While detecting and interpreting temporal patterns of non–verbal behavioral cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and human–centered interaction they are bound to experience. This paper surveys the nonverbal cues that could be present during agreement and disagreement behavioural displays and lists a number of tools that could be useful in detecting them, as well as a few publicly available databases that could be used to train these tools for analysis of spontaneous, audiovisual instances of agreement and disagreement

Texture-Based Eyebrow Recognition

Recent studies show that eyebrows can be used as a biometric or soft biometric for recognition. In some scenarios such as partially occluded or covered faces, they can be used for recognition. In this paper, we study eyebrow recognition using texture-based features. We apply features which have not been used before for eyebrow recognition such as 3-patch local binary pattern and WLD (Weber local descriptor) features. Also, we use more conventional features such as uniform LBP (Local binary pattern) and HOG (Histograms of oriented gradients). Methods are tested on both small- and large-sized datasets of images taken from FRGC database. Our experiments show that using some of these texture-based features together increases the performance significantly. We achieved more than 95% recognition accuracy for left and right eyebrows.</p

Face and Body gesture recognition for a vision-based multimodal analyser

users, computers should be able to recognize emotions, by analyzing the human&apos;s affective state, physiology and behavior. In this paper, we present a survey of research conducted on face and body gesture and recognition. In order to make human-computer interfaces truly natural, we need to develop technology that tracks human movement, body behavior and facial expression, and interprets these movements in an affective way. Accordingly in this paper, we present a framework for a vision-based multimodal analyzer that combines face and body gesture and further discuss relevant issues

IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK IN NANO SCALE ENVIRONMENT

Facial recognition systems are computer-based security systems that are able to automatically detect and identify human faces. Facial recognition has gained increasing interest in the recent decade. Over the years there have been several techniques being developed to achieve high success rate of accuracy in the identification and verification of individuals for authentication in security systems. This project experiments the concept of neural network for facial recognition that can differentiate and recognize face of image. This face recognition system begins with image pre-processing and then the output image is trained using Fuzzy c-means clustering (FCM) algorithm. FCM network learns by training the inputs, calculating the error between the real output and target output, and propagates back the error to the network to modify the weights until the desired output is obtained. After training the network, the recognition system is tested to ensure that the system can recognize the pattern of each face image. The purpose of this project is to recognize face of image for the recognition analysis using Neural Network and capture the brainwaves of the emotion recognition. This project is mainly concern with facial recognition systems using purely image processing technique