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	Learning long term face aging patterns from partially dense aging databases
	Suo, Jinli ; Chen, Xilin ; Shan, Shiguang ; Gao, Wen
	2009
英文摘要	Studies on face aging are handicapped by lack of long term dense aging sequences for model training. To handle this problem, we propose a new face aging model, which learns long term face aging patterns from partially dense aging databases. The learning strategy is based on two assumptions: (i) short term face aging pattern is relatively simple and is possible to be learned from currently available databases; (ii) long term face aging is a continuous and smoothMarkov process. Adopting a compositional face representation, our aging algorithmlearns a function-based short term aging model from real aging sequences to infer facial parameters within a short age span. Based on the predefined smoothness criteria between two overlapping short term aging patterns, we concatenate these learned short term aging patterns to build the long term aging patterns. Both the subjective assessment and objective evaluations of synthetic aging sequences validate the effectiveness of the proposed model. ?2009 IEEE.; EI; 0
语种	英语
DOI标识	10.1109/ICCV.2009.5459181
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/263087]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Suo, Jinli,Chen, Xilin,Shan, Shiguang,et al. Learning long term face aging patterns from partially dense aging databases. 2009-01-01.

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