Gender classification using GA-based adjusted order PZM and fuzzy similarity measure

An important problem in gender classification system is dealing with facial expression variations, lighting direction changes, noise presence and etc. In this paper, a new patch based method is proposed for gender classification under above conditions and when one sample from each person is available. A genetic algorithm based adjusted order Pseudo-Zernike Moment (PZM) is used to extract features of each face area. In the proposed method, a weighting scheme is utilized to determine the importance of each local area. Finally, the similarity between input image and gallery images is calculated by fuzzy similarity measure. The satisfactory experimental results show the high recognition rate of our method on the AR and FERET face databases compared to recent available approaches.

[1]  Tom V. Mathew.  Genetic Algorithm , 2022 .

[2]  Ameneh Shobeirinejad,et al.  Gender Classification Using Interlaced Derivative Patterns , 2010, 2010 20th International Conference on Pattern Recognition.

[3]  Yunus Saatci,et al.  Cascaded classification of gender and facial expression using active appearance models , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[4]  Sushil J. Louis,et al.  Genetic feature subset selection for gender classification: a comparison study , 2002, Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings..

[5]  Alireza Tavakkoli,et al.  Improvement of Zernike moment descriptors on affine transformed shapes , 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

[6]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[7]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[8]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[9]  YiDing Wang,et al.  Improving generalization for gender classification , 2008, 2008 15th IEEE International Conference on Image Processing.

[10]  Pengfei Shi,et al.  A novel fusion-based method for expression-invariant gender classification , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  Saeed Mozaffari,et al.  Gender Classification Using Single Frontal Image Per Person: Combination of Appearance and Geometric Based Features , 2010, 2010 20th International Conference on Pattern Recognition.

[12]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Bing Li,et al.  Gender classification by combining clothing, hair and facial component classifiers , 2012, Neurocomputing.

[14]  A. Martínez,et al.  The AR face databasae , 1998 .

[15]  Karim Faez,et al.  Face recognition using adaptively weighted patch PZM array from a single exemplar image per person , 2008, Pattern Recognit..

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