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Facial expression recognition through modeling age-related spatial patterns

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Abstract

In this paper we tackle the problem of expression recognition by exploiting age-related spatial facial expression patterns, which carry crucial information that have not been thoroughly exploited. First, we conduct two statistic hypothesis tests to investigate age effect on the spatial patterns of expressions and on facial expression recognition respectively. Second, we propose two methods to recognize expressions by modeling age-related spatial facial expression patterns. One is a three-node Bayesian Network to classify expressions with the help of age from person-independent geometric features. The other is to construct multiple Bayesian networks to explicitly capture the spatial facial expression patterns for different ages. For both methods, age information is used as privileged information, which is only available during training, and is exploited during training to construct a better classifier. Statistic analyses on two benchmark databases, i.e. the Lifespan and the FACES, verify the age effect on spatial patterns of expressions and on facial expression recognition. Experimental results of expression recognition demonstrate the effectiveness of the proposed methods in modelling age-related spatial patterns as well as their superior expression recognition performance to existing approaches.

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Authors and Affiliations

  1. Key Lab of Computing and Communication Software of Anhui Province School of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui, 230027, People’s Republic of China

    Shangfei Wang, Shan Wu & Zhen Gao

  2. Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute Troy, Troy, NY, 12180, USA

    Qiang Ji

Authors
  1. Shangfei Wang
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  2. Shan Wu
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  3. Zhen Gao
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  4. Qiang Ji
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Correspondence to Shangfei Wang.

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Wang, S., Wu, S., Gao, Z. et al. Facial expression recognition through modeling age-related spatial patterns. Multimed Tools Appl 75, 3937–3954 (2016). https://doi.org/10.1007/s11042-015-3107-2

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  • DOI: https://doi.org/10.1007/s11042-015-3107-2

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