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Improved image representation and sparse representation for face recognition

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Abstract

Sparse representation is of great significance to the research of face recognition. Due to factors such as illumination, angle, and facial features, different face images of the same subject have great differences under different conditions, which will cause great difficulties in image classification. To solve this problem, this paper proposes a novel image classification algorithm. The algorithm uses an improved image representation method to generate virtual samples that not only better preserve the large-scale information and global features of the original training samples, but can also be regarded as another representation of objects. Combining multiple representations of images can effectively improve the accuracy of image classification. Moreover, this paper designs a simple and efficient weight fusion scheme to fuse the original training samples and virtual samples and obtain the final classification distance of the test sample. Experimental results on multiple face databases show that the proposed algorithm has higher classification accuracy than other state-of-the-art algorithms.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Liupanshui Normal University, Liupanshui, China

    Xuqin Wei, Yun Shi, Weiyin Gong & Yanyun Guan

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  1. Xuqin Wei
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  2. Yun Shi
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  3. Weiyin Gong
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  4. Yanyun Guan
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Correspondence to Xuqin Wei.

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Wei, X., Shi, Y., Gong, W. et al. Improved image representation and sparse representation for face recognition. Multimed Tools Appl 81, 44247–44261 (2022). https://doi.org/10.1007/s11042-022-13203-5

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  • DOI: https://doi.org/10.1007/s11042-022-13203-5

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