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	Adaptive discriminant analysis for face recognition from single sample per person
	Kan, Meina ; Shan, Shiguang ; Su, Yu ; Chen, Xilin ; Gao, Wen
	2011
英文摘要	Discriminant analysis, especially Fisherface and its numerous variants, have achieved great success in face recognition. However, these methods fail to work for face recognition from Single Sample per Person (SSPP), since they need more than one sample per person to estimate the within-class scatter matrix. To break this inability of traditional discriminant analysis, our paper proposes Adaptive Discriminant Analysis (ADA). In our method, the within-class scatter matrix of each enrolled subject is estimated from his/her single sample, by inferring from a generic training set with multiple samples per person. The inference is inspired by a simple intuition that similar person follows similar within-class variations. Specifically, both kNN regression and Lasso regression are explored for this purpose. We evaluate our method on FERET database and a large real-world face database. The results are very impressive compared with dominant traditional solutions to SSPP problem. ? 2011 IEEE.; EI; 0
语种	英语
DOI标识	10.1109/FG.2011.5771397
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/328859]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Kan, Meina,Shan, Shiguang,Su, Yu,et al. Adaptive discriminant analysis for face recognition from single sample per person. 2011-01-01.

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