Face verification across age progression using discriminative methods
IEEE Transactions on Information Forensics and Security
Face verification with aging using AdaBoost and local binary patterns
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
A multi-layer model for face aging simulation
Transactions on edutainment VI
Learning gabor features for facial age estimation
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
A smile can reveal your age: enabling facial dynamics in age estimation
Proceedings of the 20th ACM international conference on Multimedia
Face verification of age separated images under the influence of internal and external factors
Image and Vision Computing
How does aging affect facial components?
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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In this paper, we present a compositional and dynamic model for face aging. The compositional model represents faces in each age group by a hierarchical And-Or graph, in which And nodes decompose a face into parts to describe details (e.g., hair, wrinkles, etc.) crucial for age perception and Or nodes represent large diversity of faces by alternative selections. Then a face instance is a transverse of the And-Or graph—parse graph. Face aging is modeled as a Markov process on the parse graph representation. We learn the parameters of the dynamic model from a large annotated face data set and the stochasticity of face aging is modeled in the dynamics explicitly. Based on this model, we propose a face aging simulation and prediction algorithm. Inversely, an automatic age estimation algorithm is also developed under this representation. We study two criteria to evaluate the aging results using human perception experiments: 1) the accuracy of simulation: whether the aged faces are perceived of the intended age group, and 2) preservation of identity: whether the aged faces are perceived as the same person. Quantitative statistical analysis validates the performance of our aging model and age estimation algorithm.