Facial age range estimation with extreme learning machines

Face image based age estimation is an approach to classify face images into one of several pre-defined age-groups. It is challenging because facial aging variation is specific to a given individual and is determined by the person's gene and many external factors, such as exposure, weather, gender, and living style. Age estimation is a multiclass problem and the number of classes to predict is quite large. There surely is facial aging trend and faces from closed age range have some similar facial aging features. It is difficult to say there are distinct facial aging features for an age. Facial aging features are found to be overlapped among nearby age groups along the aging life and are continuous in nature. In this paper, we emphasised our work on age range estimation with four pre-defined classes. We applied a fast and efficient machine learning method: extreme learning machines, to solve the age categorization problem. Local Gabor Binary Patterns, Biologically Inspired Feature and Gabor were adopted to represent face image. Age estimation was performed on three different aging datasets and experimental results are reported to demonstrate its effectiveness and robustness.

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

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

[3]  Bingbing Ni,et al.  Web image mining towards universal age estimator , 2009, ACM Multimedia.

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

[5]  Qinyu. Zhu.  Extreme Learning Machine , 2013 .

[6]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

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

[8]  Hongming Zhou,et al.  Optimization method based extreme learning machine for classification , 2010, Neurocomputing.

[9]  C. M. Reeves,et al.  Function minimization by conjugate gradients , 1964, Comput. J..

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

[11]  Hoda M. Onsi,et al.  Web Image Mining Age Estimation Framework , 2011 .

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

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

[14]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Eam Khwang Teoh,et al.  Web image mining for facial age estimation , 2013, 2013 9th International Conference on Information, Communications & Signal Processing.

[16]  Jian-Gang Wang,et al.  Age categorization via ECOC with fused gabor and LBP features , 2009, 2009 Workshop on Applications of Computer Vision (WACV).

[17]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

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

[19]  Denise C. Park,et al.  A lifespan database of adult facial stimuli , 2004, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[20]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

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

[22]  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).

[23]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .

[24]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

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

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

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

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