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Dictionary learning feature space via sparse representation classification for facial expression recognition

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Abstract

Facial expression recognition (FER) plays a significant role in human-computer interaction. In this paper, adopting a dictionary learning feature space (DLFS) via sparse representation classification (SRC), we propose a method for FER. First, we obtain a difference dictionary (DD) from the feature space by indirectly using an auxiliary neutral training set. Next, we use a dictionary learning algorithm to train the DD; this algorithm considers the samples from the DD are approximately symmetrical structure. Finally, we use SRC to represent and determine the label of each query sample. We then verify out proposed method from the perspective of training samples, dimension reduction methods and Gaussian noise variances using a variety of public databases. In addition, we compare our DLFS_SRC approach with DLFS_CRC and DLFS_LRC approaches on the Extended Cohn-Kanade (CK+) database to analyze recognition results. Our simulation experiments show that our proposed method achieved satisfying performance levels for FER.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No. 61071199) and the Natural Science Foundation of Hebei Province (No. F2016203422). In this paper, we also utilized four public databases. We therefore thank the providers of these databases.

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Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

    Zhe Sun, Zheng-ping Hu, Meng Wang & Shu-huan Zhao

  2. School of Physics and Electronic Engineering, Taishan University, Tai’an, 271021, China

    Meng Wang

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  1. Zhe Sun
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  2. Zheng-ping Hu
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  3. Meng Wang
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  4. Shu-huan Zhao
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Correspondence to Zheng-ping Hu.

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Sun, Z., Hu, Zp., Wang, M. et al. Dictionary learning feature space via sparse representation classification for facial expression recognition. Artif Intell Rev 51, 1–18 (2019). https://doi.org/10.1007/s10462-017-9554-6

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