Facial expression is an important channel for human nonverbal communication. This paper presents a novel and effective approach to automatic 3D/4D facial expression recognition based on the muscular movement model (MMM). In contrast to most of existing methods, the MMM deals with such an issue in the viewpoint of anatomy. It first automatically segments the input 3D face (frame) by localizing the corresponding points within each muscular region of the reference using iterative closest normal point. A set of features with multiple differential quantities, including coordinate, normal , and shape index values, are then extracted to describe the geometry deformation of each segmented region. Meanwhile, we analyze the importance of these muscular areas, and a score level fusion strategy is exploited to optimize their weights by the genetic algorithm in the learning step. The support vector machine and the hidden Markov model are finally used to predict the expression label in 3D and 4D, respectively. The experiments are conducted on the BU-3DFE and BU-4DFE databases, and the results achieved clearly demonstrate the effectiveness of the proposed method.Index Terms-3D/4D facial expression recognition, muscle movement model (MMM), shape representation.

Previous health studies have focused on the correlation between socioeconomic status (SES) and health. We pooled data from the Chinese Longitudinal Healthy Longevity Survey (N = 9765) conducted in 2011, and examined the association of SES and health-related behavior with elderly health in China. The cumulative health disadvantage of the elderly caused by SES can be relieved by lifelong health-related behavior. In the same SES, the odds of self-rated health (SRH) as “good,” mini-mental state examination (MMSE) as “not impaired,” and activities of daily living (ADLs) as “not impaired” among the elderly who exercised regularly, were 46.9%, 28.6%, and 62.3% lower for the elderly who rarely exercised. The elderly who started doing regular exercise from 30 years old, achieved higher SRH, ADL, and MMSE scores to some extent. The health improvement advantage for the elderly who started doing regular exercises after 60 years old, was reduced. However, the odds of SRH as “good,” MMSE as “not impaired,” and ADLs as “not impaired” were still 3.4%, 12.5%, and 17.8%, respectively, higher than the respondents who never exercised. The health-related behaviors not only promote elderly health improvement, but its duration has also been found to be associated with the extent of health improvement.

In this paper, an effective approach is proposed for automatic 4D Facial Expression Recognition (FER). It combines two growing but disparate ideas in the domain of computer vision, i.e. computing spatial facial deformations using a Riemannian method and magnifying them by a temporal filtering technique. Key frames highly related to facial expressions are first extracted from a long 4D video through a spectral clustering process, forming the Onset-Apex-Offset flow. It is then analyzed to capture the spatial deformations based on Dense Scalar Fields (DSF), where registration and comparison of neighboring 3D faces are jointly led. The generated temporal evolution of these deformations is further fed into a magnification method to amplify facial activities over time. The proposed approach allows revealing subtle deformations and thus improves the emotion classification performance. Experiments are conducted on the BU-4DFE and BP-4D databases, and competitive results are achieved compared to the state-of-the-art.

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