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Supervised bilateral two-dimensional locality preserving projection algorithm based on Gabor wavelet

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Abstract

Bilateral two-dimensional locality preserving projection (B2DLPP) is an effective method for unsupervised linear dimensionality reduction, which directly extracts face features from image matrices based on locality criterion. Motivated by B2DLPP, this paper proposes a supervised bilateral two-dimensional locality preserving projection (SB2DLPP). Different from B2DLPP, the proposed method takes into account the class information when constructing the similarity matrix. It increases inter-class distance in the projection space so that better right and left-projection matrices are obtained. Furthermore, a Gabor-based supervised bilateral two-dimensional locality preserving projection method is proposed for face recognition. Gabor wavelet representations are adopted for face images to make the proposed method robust to illumination variations and facial expression changes. Then, SB2DLPP is applied to reduce feature dimension. The performance of the proposed method is evaluated and compared with other traditional face recognition schemes on the FERET, Yale and JAFFE databases. The experiment results demonstrate the effectiveness and superiority of the proposed approach.

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Authors and Affiliations

  1. School of Information Science and Engineering, Changzhou University, Changzhou, China

    Jiuzhen Liang & Zhenjie Hou

  2. Center for Research in Computer Vision, University of Central Florida, Orlando, FL, USA

    Chen Chen

  3. School of IoT Enginering, Jiangnan University, Wuxi, China

    Xiuxiu Xu

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  1. Jiuzhen Liang
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  2. Zhenjie Hou
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  3. Chen Chen
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  4. Xiuxiu Xu
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Correspondence to Jiuzhen Liang.

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Liang, J., Hou, Z., Chen, C. et al. Supervised bilateral two-dimensional locality preserving projection algorithm based on Gabor wavelet. SIViP 10, 1441–1448 (2016). https://doi.org/10.1007/s11760-016-0950-1

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  • DOI: https://doi.org/10.1007/s11760-016-0950-1

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