Human Age-Group Estimation Using Curvelet Features

In this paper we investigate whether human digital fingerprints can be used to estimate human age-groups. To our knowledge, human age-group estimation using digital fingerprints have not been addressed formally. Human age-group estimation can be applied in the areas of online child protection, age based access control or customized services based on estimated age groups. Motivated by the fact that human digital fingerprint vary in width ranging from birth to adulthood but pattern remains the same, we have developed a procedure to extract discriminating features using Curve let Transform to classify fingerprints into three age groups. Experimental results show the feasibility of our method which can be used to protect children over cyberspace by automatically customizing their access according to their age group.

[1]  Laurent Demanet,et al.  Fast Discrete Curvelet Transforms , 2006, Multiscale Model. Simul..

[2]  Yun Fu,et al.  Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression , 2008, IEEE Transactions on Image Processing.

[3]  Yun Fu,et al.  Human Age Estimation With Regression on Discriminative Aging Manifold , 2008, IEEE Transactions on Multimedia.

[4]  K. Ricanek,et al.  Aspects of Age Variation in Facial Morphology Affecting Biometrics , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[5]  Convention on the Rights of the Child, 20 November 1989. , 1989, Annual review of population law.

[6]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[7]  D. Berry,et al.  Perceiving character in faces: the impact of age-related craniofacial changes on social perception. , 1986, Psychological bulletin.

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

[9]  Minh N. Do,et al.  Contourlets: a directional multiresolution image representation , 2002, Proceedings. International Conference on Image Processing.

[10]  Rama Chellappa,et al.  Face Verification Across Age Progression , 2006, IEEE Transactions on Image Processing.

[11]  Yun Fu,et al.  Locally Adjusted Robust Regression for Human Age Estimation , 2008, 2008 IEEE Workshop on Applications of Computer Vision.

[12]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[13]  K. Ricanek,et al.  The effect of normal adult aging on standard PCA face recognition accuracy rates , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[14]  Angshul Majumdar,et al.  A comparative study in wavelets, curvelets and contourlets as feature sets for pattern recognition , 2009, Int. Arab J. Inf. Technol..

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

[16]  Kathryn A. Kamp,et al.  Discovering Childhood: Using Fingerprints to Find Children in the Archaeological Record , 1999, American Antiquity.

[17]  T. Waterston,et al.  The Convention on the Rights of the Child , 2006, The Lancet.

[18]  ZhouZhi-Hua,et al.  Automatic Age Estimation Based on Facial Aging Patterns , 2007 .