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Dynamics Analysis of Facial Expressions for Person Identification

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 5702))

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Abstract

We propose a new method for analyzing the dynamics of facial expressions to identify persons using Active Appearance Models and accurate facial feature point tracking. Several methods have been proposed to identify persons using facial images. In most methods, variations in facial expressions are one trouble factor. However, the dynamics of facial expressions are one measure of personal characteristics. In the proposed method, facial feature points are automatically extracted using Active Appearance Models in the first frame of each video. They are then tracked using the Lucas-Kanade based feature point tracking method. Next, a temporal interval is extracted from the beginning time to the ending time of facial expression changes. Finally, a feature vector is obtained. In the identification phase, an input feature vector is classified by calculating the distance between the input vector and the training vectors using dynamic programming matching. We show the effectiveness of the proposed method using smile videos from the MMI Facial Expression Database.

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Authors and Affiliations

  1. Graduate School of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kouhoku-ku, Yokohama, Kanagawa, 223-8522, Japan

    Hidenori Tanaka & Hideo Saito

Authors
  1. Hidenori Tanaka

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  2. Hideo Saito

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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Einsteinstrasse 62, 48149, Münster, Germany

    Xiaoyi Jiang

  2. Institute of Mathematics and Computing Science, University of Groningen, Nijenborgh 9, 9747, Groningen, AG, The Netherlands

    Nicolai Petkov

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© 2009 Springer-Verlag Berlin Heidelberg

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Tanaka, H., Saito, H. (2009). Dynamics Analysis of Facial Expressions for Person Identification. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_13

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