Abstract
Stimulated by multi-task learning method, this paper proposes an algorithm of Feature Selection based on Multi-Task Learning (FS-MTL) for ethnicity and gender recognition with face images. The proposed FS-MTL selects the common features which are shared by multi-tasks are based on the sparse optimization solution of group Least Absolute Shrinkage and Selection Operator (LASSO). Compared with either the classic feature selection algorithm or the single task feature selection, the proposed algorithm can get higher recognition rate through sharing the related information among tasks. At the same time, the stability analysis is introduced to feature selection. With given stability metrics, the results of experiments show that features selected with the proposed algorithm are more stable.
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Yu, C., Fang, Y., Li, Y. (2014). Multi-Task Learning for Face Ethnicity and Gender Recognition. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds) Biometric Recognition. CCBR 2014. Lecture Notes in Computer Science, vol 8833. Springer, Cham. https://doi.org/10.1007/978-3-319-12484-1_15
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DOI: https://doi.org/10.1007/978-3-319-12484-1_15
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