A Concatenational Graph Evolution Aging Model

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 decomposi...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 34; no. 11; pp. 2083 - 2096
Main Authors:	Suo, Jinli, Chen, Xilin, Shan, Shiguang, Gao, Wen, Dai, Qionghai
Format:	Journal Article
Language:	English
Published:	Los Alamitos, CA IEEE 01.11.2012 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Active appearance model Aging Aging - pathology Aging - physiology aging model evaluation ANOVA Applied sciences Artificial intelligence Computational modeling Computer science; control theory; systems Computer Simulation Construction Correlation Data models Decomposition Evolution Exact sciences and technology Face Face - anatomy & histology Face - physiology Face aging Graphical representations Graphs Humans Information retrieval. Graph Learning and adaptive systems long-term aging Mathematical models Models, Anatomic Models, Biological Models, Statistical Muscles Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry short-term aging Strategy Studies Temporal logic Theoretical computing Short term Markov process Image processing Modeling long-term aging ANOVA Face aging Facies Database Sampling Computer vision Probabilistic approach Face recognition Ageing Graph theory Anatomy Variance analysis Long term short-term aging Guidance Graphics Concatenation aging model evaluation Graph method Artificial intelligence
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
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Summary:	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.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3
ISSN:	0162-8828 1939-3539 2160-9292 1939-3539
DOI:	10.1109/TPAMI.2012.22