Abstract
In this paper, a new algorithm named Adaptively Weighted Structure Preserved Projections (Aw-SPP) is proposed for face recognition. Firstly, the configural structure relationship of sub-images in each face image is preserved in Aw-SPP. Then, an adaptive non-negative weight vector is introduced to take different contributions of various sub-pattern sets into account, which combines the Laplacian matrices obtained by different sub-pattern sets. Simultaneously, a Laplacian penalty constraint is also incorporated to preserve the intrinsic 2D structure of each sub-image. Finally, the procedures of feature extraction and non-negative weight vector learning are integrated into a unified framework. Moreover, an efficient iterative algorithm is designed to optimize our objective function. To validate the feasibility and effectiveness of the proposed approach, extensive experiments are conducted on three face databases (Extended YaleB, CMU PIE and AR). Experimental results demonstrate that the proposed Aw-SPP outperforms some other state of the art algorithms.
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Acknowledgment
This work is supported by National Natural Science Foundation of China (Nos. 61403078 and 61562044), Science and Technology Research Project of Liaoning Province Education Department (No. L2014450).
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Yi, Y., Zhou, W., Shi, Y., Luo, G., Wang, J. (2016). Adaptively Weighted Structure Preserved Projections for Face Recognition. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_39
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DOI: https://doi.org/10.1007/978-981-10-3002-4_39
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