This paper investigates the use of facial gestures for identity recognition. This is the first time that such a quantitative evaluation is conducted, comparing the analyses of 2D versus 3D dynamic data of verbal and nonverbal facial actions. Suitable data processing and feature extraction methods

Abstract — This paper describes a novel approach to building models of temporal dynamics for facial animation with applications in performing perceptual testing of trustworthiness. A vital component of the system is a method to bring two image sequences into temporal alignment. Our approach

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-resolution facial expression space is learnt from a set of 3D face scans as a person specific morphable model. Sparse 3D face points sampled at the motion capture marker positions are used to build a corresponding low-resolution expression space to represent the facial dynamics from motion capture. Radial basis

As the video-realism of facial models improves there is a need to study realistic facial dynamics based on the psychological analysis of real people. There is a difference between simply recognising a facial expression and truly believing its implied emotion. We have constructed models that allow

Detecting cooperative partners in situations that have financial stakes is crucial to successful social exchange. We tested whether humans are sensitive to subtle facial dynamics of counterparts when deciding whether to trust and cooperate. Participants played a 2-person trust game prior to which

of muscle actions. Fortunately, animators are now able to digitize facial geometries through the use of scanning range sensors and animate them through the dynamic simulation of facial tissues and muscles. However, these techniques require considerableuser input to construct facial models of individuals

Research has shown that the dynamics of facial motion are important in the perception of gender, identity, and emotion. In this paper we show that it is possible to use a multilinear tensor framework to extract facial motion signatures and to cluster these signatures by gender or by emotion. Here

Abstract — Research has shown that the dynamics of facial motion are important in the perception of gender, identity, and emotion. In this paper we show that it is possible to use a multi-linear tensor framework to extract facial motion signatures and to cluster these signatures by gender

examined the effect of motion on perception of subtle facial expressions and tested the hypotheses that Motion improves affect judgment by (1) providing denser sampling of the expression, (2) providing dynamic information, (3) facilitating configural processing, and (4) enhancing the perception of change