3D Age Progression Prediction in Children's Faces with a Small Exemplar-Image Set

This work aims to develop a system for predicting age progression in children’s faces from a small exemplar-image set, which is a critical task to assist in the search for missing children. The proposed method consists of a facial component extraction module, a facial component distance measurement module, and a face synthesis module. It is developed based on the assumption that two similar facial components of two children will retain similar when they grow up. Two different distance measures, namely the learning-based Mahalanobis distance and the curvature-weighted plus bending-energy distance, are employed to select similar facial components from an aging database. The growth curve of each facial component is used to predict the shape, size, and location of each component at a different age. The thin plate spline method is applied to synthesize a 3D face model from the predicted components by minimizing the bending energy. Experiments are conducted to test the proposed method with various subjects and the results show that the proposed method yields promising results.

[1]  Yun Fu,et al.  Human age estimation using bio-inspired features , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Shuhua Lai,et al.  Growth Simulation of Facial/Head Model from Childhood to Adulthood , 2010 .

[3]  A. Gray.  Modern Differential Geometry of Curves and Surfaces , 1993 .

[4]  Timothy F. Cootes,et al.  Toward Automatic Simulation of Aging Effects on Face Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  A. Albert,et al.  A review of the literature on the aging adult skull and face: implications for forensic science research and applications. , 2007, Forensic science international.

[6]  Niels da Vitoria Lobo,et al.  Age Classification from Facial Images , 1999, Comput. Vis. Image Underst..

[7]  Anonymous Submission,et al.  Automatic child-face age-progression based on heritability factors of familial faces , 2009, 2009 First IEEE International Conference on Biometrics, Identity and Security (BIdS).

[8]  Rama Chellappa,et al.  Computational methods for modeling facial aging: A survey , 2009, J. Vis. Lang. Comput..

[9]  Thomas R. Alley,et al.  Social and Applied Aspects of Perceiving Faces , 2013 .

[10]  Shiguang Shan,et al.  A Compositional and Dynamic Model for Face Aging , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Wen Gao,et al.  Learning long term face aging patterns from partially dense aging databases , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[12]  Christopher J. Solomon,et al.  Aging the human face - a statistically rigorous approach , 2005 .

[13]  Zhi-Hua Zhou,et al.  Automatic Age Estimation Based on Facial Aging Patterns , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Yi-Ping Hung,et al.  Ordinal hyperplanes ranker with cost sensitivities for age estimation , 2011, CVPR 2011.

[15]  Maulin R. Gandhi.  A Method for Automatic Synthesis of Aged Human Facial Images , 2004 .

[16]  Yixiong Liang,et al.  Age Simulation in Young Face Images , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[17]  Yun Fu,et al.  Age Synthesis and Estimation via Faces: A Survey , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Rama Chellappa,et al.  Modeling Age Progression in Young Faces , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[19]  Hans-Peter Seidel,et al.  Prediction of Individual Non‐Linear Aging Trajectories of Faces , 2007, Comput. Graph. Forum.

[20]  Inderjit S. Dhillon,et al.  Information-theoretic metric learning , 2006, ICML '07.

[21]  Dit-Yan Yeung,et al.  Multi-task warped Gaussian process for personalized age estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Matthew G. Rhodes,et al.  Age estimation of faces: a review† , 2009 .

[23]  C. Cacou.  Anthropometry of the head and face , 1995 .

[24]  Andreas Lanitis,et al.  Comparative Evaluation of Automatic Age-Progression Methodologies , 2008, EURASIP J. Adv. Signal Process..

[25]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Song-Chun Zhu,et al.  A Multi-Resolution Dynamic Model for Face Aging Simulation , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.