A study on automatic age estimation using a large database

In this paper we study some problems related to human age estimation using a large database. First, we study the influence of gender on age estimation based on face representations that combine biologically-inspired features with manifold learning techniques. Second, we study age estimation using smaller gender and age groups rather than on all ages. Significant error reductions are observed in both cases. Based on these results, we designed three frameworks for automatic age estimation that exhibit high performance. Unlike previous methods that require manual separation of males and females prior to age estimation, our work is the first to estimate age automatically on a large database. Furthermore, a data fusion approach is proposed using one of the frameworks, which gives an age estimation error more than 40% smaller than previous methods.

[1]  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..

[2]  Shuicheng Yan,et al.  Ranking with Uncertain Labels , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[3]  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.

[4]  Lior Wolf,et al.  Using Biologically Inspired Features for Face Processing , 2007, International Journal of Computer Vision.

[5]  V. Sudhakar,et al.  Electronic Customer Relationship Management (E-CRM) , 2009 .

[6]  David G. Lowe,et al.  University of British Columbia. , 1945, Canadian Medical Association journal.

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

[8]  Ye Xu,et al.  Estimating Human Age by Manifold Analysis of Face Pictures and Regression on Aging Features , 2007, 2007 IEEE International Conference on Multimedia and Expo.

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

[10]  Dorin Comaniciu,et al.  Image based regression using boosting method , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[11]  Wen Gao,et al.  Design sparse features for age estimation using hierarchical face model , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[12]  Thomas Serre,et al.  Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Yun Fu,et al.  A Probabilistic Fusion Approach to human age prediction , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[14]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Yu Zhang,et al.  Learning from facial aging patterns for automatic age estimation , 2006, MM '06.

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

[17]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

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

[19]  C. Christodoulou,et al.  Comparing different classifiers for automatic age estimation , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Stephen Lin,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Tetsunori Kobayashi,et al.  Subspace-based age-group classification using facial images under various lighting conditions , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[22]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[23]  Rama Chellappa,et al.  Face Verification Across Age Progression , 2006, IEEE Trans. Image Process..

[24]  Shuicheng Yan,et al.  Learning Auto-Structured Regressor from Uncertain Nonnegative Labels , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[25]  Haizhou Ai,et al.  Demographic Classification with Local Binary Patterns , 2007, ICB.

[26]  Hiroyasu Koshimizu,et al.  Method for estimating and modeling age and gender using facial image processing , 2001, Proceedings Seventh International Conference on Virtual Systems and Multimedia.

[27]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[28]  M. Bannier.  [Aging of the face]. , 1993, Soins. Chirurgie.

[29]  Kun Zhou,et al.  Locality Sensitive Discriminant Analysis , 2007, IJCAI.

[30]  Shuicheng Yan,et al.  Extracting age information from local spatially flexible patches , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[31]  Ming Liu,et al.  Regression from patch-kernel , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Niels da Vitoria Lobo,et al.  Age classification from facial images , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

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

[34]  Jiawei Han,et al.  Orthogonal Laplacianfaces for Face Recognition , 2006, IEEE Transactions on Image Processing.

[35]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..