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HSR: L 1/2-regularized sparse representation for fast face recognition using hierarchical feature selection

  • Extreme Learning Machine and Applications
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Abstract

In this paper, we propose a novel method for fast face recognition called L 1/2-regularized sparse representation using hierarchical feature selection. By employing hierarchical feature selection, we can compress the scale and dimension of global dictionary, which directly contributes to the decrease of computational cost in sparse representation that our approach is strongly rooted in. It consists of Gabor wavelets and extreme learning machine auto-encoder (ELM-AE) hierarchically. For Gabor wavelets’ part, local features can be extracted at multiple scales and orientations to form Gabor-feature-based image, which in turn improves the recognition rate. Besides, in the presence of occluded face image, the scale of Gabor-feature-based global dictionary can be compressed accordingly because redundancies exist in Gabor-feature-based occlusion dictionary. For ELM-AE part, the dimension of Gabor-feature-based global dictionary can be compressed because high-dimensional face images can be rapidly represented by low-dimensional feature. By introducing L 1/2 regularization, our approach can produce sparser and more robust representation compared to L 1-regularized sparse representation-based classification (SRC), which also contributes to the decrease of the computational cost in sparse representation. In comparison with related work such as SRC and Gabor-feature-based SRC, experimental results on a variety of face databases demonstrate the great advantage of our method for computational cost. Moreover, we also achieve approximate or even better recognition rate.

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Acknowledgments

The authors would like to thank Dr Jun Zhou at Griffith University for helpful and excellent discussions and comments. This work is partially supported by Natural Science Foundation of China (41176076, 31202036 and 51075377).

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Authors and Affiliations

  1. School of Information Science and Engineering, Ocean University of China, Qingdao, 266000, Shandong, China

    Bo Han, Bo He, Tingting Sun, Mengmeng Ma & Yue Shen

  2. School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, 310018, Zhejiang, China

    Tianhong Yan

  3. Department of Mechanical and Industrial Engineering and the Iowa Informatics Initiative, The University of Iowa, Iowa City, IA, 52242-1527, USA

    Amaury Lendasse

  4. Arcada University of Applied Sciences, 00550, Helsinki, Finland

    Amaury Lendasse

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  1. Bo Han
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  2. Bo He
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  3. Tingting Sun
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  4. Tianhong Yan
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  5. Mengmeng Ma
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  6. Yue Shen
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  7. Amaury Lendasse
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Corresponding authors

Correspondence to Bo He or Tianhong Yan.

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Han, B., He, B., Sun, T. et al. HSR: L 1/2-regularized sparse representation for fast face recognition using hierarchical feature selection. Neural Comput & Applic 27, 305–320 (2016). https://doi.org/10.1007/s00521-015-1907-y

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  • DOI: https://doi.org/10.1007/s00521-015-1907-y

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