Age estimation based on extended non-negative matrix factorization

Previous studies suggested that local appearance-based methods are more efficient than geometric-based and holistic methods for age estimation. This is mainly due to the fact that age information are usually encoded by the local features such as wrinkles and skin texture on the forehead or at the eye corners. However, the variations of theses features caused by other factors such as identity, expression, pose and lighting may be larger than that caused by aging. Thus, one of the key challenges of age estimation lies in constructing a feature space that could successfully recovers age information while ignoring other sources of variations. In this paper, non-negative matrix factorization (NMF) is extended to learn a localized non-overlapping subspace representation for age estimation. To emphasize the appearance variation in aging, one individual extended NMF subspace is learned for each age or age group. The age or age group of a given face image is then estimated based on its reconstruction error after being projected into the learned age subspaces. Furthermore, a coarse to fine scheme is employed for exact age estimation, so that the age is estimated within the pre-classified age groups. Cross-database tests are conducted using FG-NET and MORPH databases to evaluate the proposed method. Experimental results have demonstrated the efficacy of the method.

[1]  A. Gunay,et al.  Automatic age classification with LBP , 2008, 2008 23rd International Symposium on Computer and Information Sciences.

[2]  Yuxiao Hu,et al.  Subspace learning for human head pose estimation , 2008, 2008 IEEE International Conference on Multimedia and Expo.

[3]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[4]  Mohan M. Trivedi,et al.  Head Pose Estimation in Computer Vision: A Survey , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[6]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[7]  Stan Z. Li,et al.  Learning multiview face subspaces and facial pose estimation using independent component analysis , 2005, IEEE Transactions on Image Processing.

[8]  Jian-Gang Wang,et al.  EM enhancement of 3D head pose estimated by point at infinity , 2007, Image Vis. Comput..

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

[10]  Karl Ricanek,et al.  MORPH: a longitudinal image database of normal adult age-progression , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[11]  Stan Z. Li,et al.  Learning spatially localized, parts-based representation , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[13]  Larry S. Davis,et al.  Computing 3-D head orientation from a monocular image sequence , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[14]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[15]  Patrik O. Hoyer,et al.  Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..

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

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

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

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

[20]  Feng Gao,et al.  Face Age Classification on Consumer Images with Gabor Feature and Fuzzy LDA Method , 2009, ICB.

[21]  Simon Kriegel,et al.  The Application of Active Appearance Models to Comprehensive Face Analysis , 2007 .

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

[23]  P. Paatero,et al.  Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values† , 1994 .

[24]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[25]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

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

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

[28]  Katsuhiko Sakaue,et al.  Head pose estimation by nonlinear manifold learning , 2004, ICPR 2004.

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