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Face Recognition and Micro-expression Recognition Based on Discriminant Tensor Subspace Analysis Plus Extreme Learning Machine

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Abstract

In this paper, a novel recognition algorithm based on discriminant tensor subspace analysis (DTSA) and extreme learning machine (ELM) is introduced. DTSA treats a gray facial image as a second order tensor and adopts two-sided transformations to reduce dimensionality. One of the many advantages of DTSA is its ability to preserve the spatial structure information of the images. In order to deal with micro-expression video clips, we extend DTSA to a high-order tensor. Discriminative features are generated using DTSA to further enhance the classification performance of ELM classifier. Another notable contribution of the proposed method includes significant improvements in face and micro-expression recognition accuracy. The experimental results on the ORL, Yale, YaleB facial databases and CASME micro-expression database show the effectiveness of the proposed method.

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Notes

  1. The matlab code can be downloaded from http://www3.ntu.edu.sg/home/egbhuang/.

  2. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.

  3. http://cvc.yale.edu/projects/yalefaces/yalefaces.html.

  4. http://www.zjucadcg.cn/dengcai/Data/FaceData.html.

  5. The matlab code can be downloaded from http://www.zjucadcg.cn/dengcai/Data/data.html.

  6. The onset is the first frame which changes from the baseline (usually neutral facial expressions). The apex is the one that reaches highest intensity of the facial expression. The offset is the last frame of the expression (before turning back to a neutral facial expression). Sometimes the facial expressions faded very slowly, and the changes between frames were very difficult to detect by eyes. For such offset frames, the coders only coded the last obvious frame as the offset frame while ignore the nearly imperceptible changing frames.

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Acknowledgments

This work was supported in part by grants from 973 Program (2011CB302201), the National Natural Science Foundation of China (61075042, 61175023) and China Postdoctoral Science Foundation funded project (2012M520428).

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Authors and Affiliations

  1. State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China

    Su-Jing Wang, Wen-Jing Yan, Yu-Hsin Chen & Xiaolan Fu

  2. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Su-Jing Wang

  3. College of Physics and Electronic Information, Wenzhou University, Wenzhou, 325035, Zhejiang, China

    Hui-Ling Chen

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  1. Su-Jing Wang
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Correspondence to Su-Jing Wang.

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Wang, SJ., Chen, HL., Yan, WJ. et al. Face Recognition and Micro-expression Recognition Based on Discriminant Tensor Subspace Analysis Plus Extreme Learning Machine. Neural Process Lett 39, 25–43 (2014). https://doi.org/10.1007/s11063-013-9288-7

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