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A Concatenational Graph Evolution Aging Model
Nov. 2012 (vol. 34 no. 11)
pp. 2083-2096
Jinli Suo, Dept. of Autom., Tsinghua Univ., Beijing, China
Xilin Chen, Key Lab. of Intell. Inf. Process., Inst. of Comput. Technol., Beijing, China
Shiguang Shan, Key Lab. of Intell. Inf. Process., Inst. of Comput. Technol., Beijing, China
Wen Gao, Key Lab. of Machine Perception, Peking Univ., Beijing, China
Qionghai Dai, Dept. of Autom., Tsinghua Univ., Beijing, China
Modeling the long-term face aging process is of great importance for face recognition and animation, but there is a lack of sufficient long-term face aging sequences for model learning. To address this problem, we propose a CONcatenational GRaph Evolution (CONGRE) aging model, which adopts decomposition strategy in both spatial and temporal aspects to learn long-term aging patterns from partially dense aging databases. In spatial aspect, we build a graphical face representation, in which a human face is decomposed into mutually interrelated subregions under anatomical guidance. In temporal aspect, the long-term evolution of the above graphical representation is then modeled by connecting sequential short-term patterns following the Markov property of aging process under smoothness constraints between neighboring short-term patterns and consistency constraints among subregions. The proposed model also considers the diversity of face aging by proposing probabilistic concatenation strategy between short-term patterns and applying scholastic sampling in aging prediction. In experiments, the aging prediction results generated by the learned aging models are evaluated both subjectively and objectively to validate the proposed model.
Index Terms:
Aging,Face,Active appearance model,Correlation,Computational modeling,Data models,Muscles,ANOVA,Face aging,aging model evaluation,long-term aging,short-term aging
Citation:
Jinli Suo, Xilin Chen, Shiguang Shan, Wen Gao, Qionghai Dai, "A Concatenational Graph Evolution Aging Model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2083-2096, Nov. 2012, doi:10.1109/TPAMI.2012.22
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