Age Regression Based on Local Image Features

Human age estimation using facial image is becoming more and more investigated because of its potential applications in many areas such as multimedia communication and human computer interaction. Since many factors contribute to the aging process like gender, race, health, living style, the current age estimation performance for computer vision systems is still not efficient enough for practical use. In this paper, we addressed the problem of age estimation from single facial gray-scale image since the color information appeared as not significant in considered low resolution images. Local and global Discrete Cosinus Transformation (DCT) are used for feature extraction allowing thus a first dimensionnality reduction through this discriminative representation. A second reduction of dimensionality has been obtained through principal component analysis(PCA). A linear regression function has been learned and tested on different large databases extracted from MORPH. Experimental results have shown some encouraging results.

[1]  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.

[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]  Zhi-Hua Zhou,et al.  Facial age estimation by nonlinear aging pattern subspace , 2008, ACM Multimedia.

[4]  Edward A. Patrick,et al.  A Generalized k-Nearest Neighbor Rule , 1970, Inf. Control..

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

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

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

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

[9]  S. Maitra,et al.  Principle Component Analysis and Partial Least Squares: Two Dimension Reduction Techniques for Regression , 2008 .

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

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

[12]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[13]  Kate Smith-Miles,et al.  Facial age estimation by multilinear subspace analysis , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  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).

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

[16]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

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

[18]  Durga L. Shrestha,et al.  Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression , 2006, Neural Computation.