Abstract
Face recognition is an active topic in recognition systems, while face occlusion is one of the most challenging problems for recognition. Recently, robust sparse coding achieved the state-of-the-art performance, especially when dealing with occluded images. However, robust sparse coding is known that only guarantees the coefficient is global sparse when solving sparse coefficients. In this paper, we enable the elements in the object region to approximate global maximum by fitting the distribution of elements in the object region with successful recognition. The efficacy of the proposed approach is verified on publicly available databases. Furthermore, our method can achieve much better performance when the training samples are limited.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (NO.61171077), University Science Research Project of Jiangsu Province (NO.12KJB510025), Nantong University Undergraduate Training Program for Innovation (NO.2013067), and the Natural Sciences and Engineering Research Council of Canada.
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Xu, Z., Li, H., Jin, X., Suen, C.Y. (2015). Face Recognition Based on Maximum Sparse Coefficients of Object Region. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46469-4_5
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DOI: https://doi.org/10.1007/978-3-662-46469-4_5
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