Abstract
A novel discriminant locality preserving dictionary learning (DLPDL) algorithm for face recognition is proposed in this paper. In order to achieve better performance and less computation, dimensionality reduction is applied on original image samples. Most of the proposed dictionary learning methods learn features and dictionary, however, the inner structure of feature is hardly considered. Therefore, by incorporating discriminant locality preserving criteria into dictionary learning, the margin of coefficients distance between between-class and within-class is encourage to be large in order to enhance the classification ability and gain discriminative information. What is more, the local structure of the feature is also preserved, which is very vital in face recognition performance. Our experiments on Extended Yale B, AR and CMU face database demonstrated the proposed algorithm has higher recognition performance than other dictionary learning based classification methods.
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Acknowledgements
This work is supported by grants by the Shandong Provincial Key R&D Program (2016ZDJS01A12), the National Natural Science Foundation of China (Grant No. 61303199).
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Feng, G., Li, H., Dong, J., Chen, X. (2017). Sparse Representation Based on Discriminant Locality Preserving Dictionary Learning for Face Recognition. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_34
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DOI: https://doi.org/10.1007/978-3-319-68935-7_34
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