Gender classification from infants to seniors

Many believe that gender classification is a solved problem, however, gender classification for children is a very difficult problem that has not been adequately addressed by the research community. In this work we demonstrate this fact and present a system that performs gender classification on children that outperforms humans. Motivated by the significant improvement in model selection for age estimation [5], we investigate a robust gender classification system via model selection and evaluate the systems using leave-one-person-out cross-validation and 5-fold cross-validation schemes on FG-NET database. Furthermore, this work develops a novel operator, graph gender preserving, to build a neighborhood graph for locality preserving projection for gender classification.

[1]  Cuixian Chen,et al.  Generalized multi-ethnic face age-estimation , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[2]  Roope Raisamo,et al.  Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Ming-Hsuan Yang,et al.  Gender classification using support vector machines , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

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

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

[6]  Heather A. Wild,et al.  Recognition and sex categorization of adults' and children's faces: examining performance in the absence of sex-stereotyped cues. , 2000, Journal of experimental child psychology.

[7]  A. O'Toole,et al.  Sex Classification is Better with Three-Dimensional Head Structure Than with Image Intensity Information , 1997, Perception.

[8]  Harry Wechsler,et al.  Gender and ethnic classification of human faces using hybrid classifiers , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[9]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[10]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[11]  Ming Li,et al.  An Experimental Study on Automatic Face Gender Classification , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[12]  Cuixian Chen,et al.  Face age estimation using model selection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[13]  Wei Gao,et al.  Face Gender Classification on Consumer Images in a Multiethnic Environment , 2009, ICB.

[14]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

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