Abstract
For a few years, plenty of face recognition algorithms, such as deep learning, have been in hot pursuit with the trend of the technology, while dictionary learning algorithm is still out of the woods for the sake of its higher robustness to occlusion and light. In this paper, we propose to learn a discriminative structured dictionary with constraint named as multi-label to suppress representations for different classes, as well as Laplacian Eigenmaps to encourage the representations for the same class to be close to each other. Demonstrated by the results of the experiments, our proposed dictionary learning methods intend to achieve better classification performance and higher computational efficiency compared to the existing algorithms.
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Zhu, Y. (2020). Discriminative Structured Dictionary Learning for Face Recognition. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-13-6508-9_18
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DOI: https://doi.org/10.1007/978-981-13-6508-9_18
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