Joint estimation of age, gender and ethnicity: CCA vs. PLS

Human age, gender and ethnicity are valuable demographic information about a population. These measures are also considered important soft biometric traits for human recognition or search. Usually the three traits are studied separately. A recent study [9] shows that the three traits can be estimated simultaneously based on a multi-label regression formulation. The linear and kernel partial least squares (PLS) models are adopted to solve the multi-label regression problem in [9]. In this study, we investigate the canonical correlation analysis (CCA) based methods, including linear CCA, regularized CCA (rCCA), and kernel CCA (KCCA), and compare to the PLS models in solving the joint estimation problem. Interestingly, we found a consistent ranking of the five methods in estimating age, gender, and ethnicity. More importantly, we found that the CCA based methods can derive an extremely low dimensionality in estimating age, gender and ethnicity, which has not been shown in previous research, to the best of our knowledge. The experiments are conducted on a very large database of more than 55,000 face images.

[1]  Shumeet Baluja,et al.  Boosting Sex Identification Performance , 2005, International Journal of Computer Vision.

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

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

[4]  Tae-Kyun Kim,et al.  Tensor Canonical Correlation Analysis for Action Classification , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  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).

[6]  John Shawe-Taylor,et al.  A Correlation Approach for Automatic Image Annotation , 2006, ADMA.

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

[8]  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).

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

[10]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[11]  Harry Wechsler,et al.  Gender and ethnic classification of face images , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[12]  Masato Kawade,et al.  Ethnicity estimation with facial images , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

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

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

[15]  R. Rosipal.  Nonlinear Partial Least Squares An Overview , 2011 .

[16]  Harry Wechsler,et al.  Mixture of experts for classification of gender, ethnic origin, and pose of human faces , 2000, IEEE Trans. Neural Networks Learn. Syst..

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

[18]  D. Jacobs,et al.  Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch , 2011, CVPR 2011.

[19]  Ming-Hsuan Yang,et al.  Learning Gender with Support Faces , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Terrence J. Sejnowski,et al.  SEXNET: A Neural Network Identifies Sex From Human Faces , 1990, NIPS.

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

[22]  Roman Rosipal,et al.  Overview and Recent Advances in Partial Least Squares , 2005, SLSFS.

[23]  Guodong Guo,et al.  Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression , 2011, CVPR 2011.

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

[25]  H. Hotelling.  Relations Between Two Sets of Variates , 1936 .

[26]  S. Shan,et al.  Maximizing intra-individual correlations for face recognition across pose differences , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Paul A. Viola,et al.  A unified learning framework for real time face detection and classification , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[28]  Jieping Ye,et al.  Canonical Correlation Analysis for Multilabel Classification: A Least-Squares Formulation, Extensions, and Analysis , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[30]  Brunelli Poggio,et al.  HyberBF Networks for Gender Classification , 1992 .

[31]  Roman Rosipal,et al.  Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space , 2002, J. Mach. Learn. Res..

[32]  Guodong Guo,et al.  Human age estimation: What is the influence across race and gender? , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[33]  H. Wold.  Path Models with Latent Variables: The NIPALS Approach , 1975 .

[34]  Yun Fu,et al.  A study on automatic age estimation using a large database , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

[36]  Anil K. Jain,et al.  Ethnicity identification from face images , 2004, SPIE Defense + Commercial Sensing.