's independent muscle action groups, as well as an accurate estimate of facial motion. Previous efforts at analysis of facial expression have been based on the Facial Action Coding System (FACS), a representation developed in order to allow human psychologists to code expression from static pictures. To avoid

Mug-shot search is the classic example of the general problem of searching a large facial image database when starting out with only a mental image of the sought-after face. We have implemented a prototype content-based image retrieval system that integrates composite face creation methods with a

is the ability to understand the emotional state of the person. The most expressive way humans display emotions is through facial expressions. In this work we report on several advances we have made in building a system for classification of facial expressions from continuous video input. We introduce and test

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will provide tips about the identity of the suspect. This method of identifying suspects is slow, tedious, and may not lead to the timely apprehension of a suspect. Hence, there is a need for a method that can automatically and efficiently match facial composites to large police mugshot databases. Because

Previous efforts at facial expression recognition have been based on the Facial Action Coding System (FACS), a representation developed in order to allow human psychologists to code expression from static facial "mugshots." In this paper we develop new, more accurate representations

Automatic recognition of facial gestures (i.e., facial muscle activity) is rapidly becoming an area of intense interest in the research field of machine vision. In this paper, we present an automated system that we developed to recognize facial gestures in static, frontal- and/or profile-view color

neuroimaging, we tested two hypotheses. First, we tested whether the amygdala has a neural response to sad and/or angry facial expressions. Secondly, we tested whether the orbitofrontal cortex has a specific neural response to angry facial expressions. Volunteer subjects were scanned, using PET, while

Traditionally, human facial expressions have been studied using either 2D static images or 2D video sequences. The 2D-based analysis is incapable of handing large pose variations. Although 3D modeling techniques have been extensively used for 3D face recognition and 3D face animation, barely any

Face recognition has established itself as an important subbranch of pattern recognition within the field of computer science. Many state-of-the-art systems have focused on the task of recognizing frontal views or images with just slight variations in head pose and facial expression of people