Gender recognition from faces using bandlet and local binary patterns

In this paper, multi-scale bandlet and local binary pattern (LBP) based method for gender recognition from faces is proposed. Bandlet is one of the multi-resolution techniques that can adapt the orientation of the edges of the face images, and thereby can better capture the texture of a face image. After extracting bandlet coefficients from face images at different scales, LBP is applied to create a histogram, which is used as the feature to a minimum distance classifier. The experiments are performed using FERET grayscale face database, and the highest accuracy of 99.13% is obtained with the proposed method.

[1]  Jingwen Yan,et al.  A novel image fusion algorithm based on bandelet transform , 2007 .

[2]  Yuchun Fang,et al.  Improving LBP features for gender classification , 2008, 2008 International Conference on Wavelet Analysis and Pattern Recognition.

[3]  Michael A. Webster,et al.  Investigating the Impact of Face Categorization on Recognition Performance , 2005, ISVC.

[4]  Roope Raisamo,et al.  An experimental comparison of gender classification methods , 2008, Pattern Recognit. Lett..

[5]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Luís A. Alexandre.  Gender recognition: A multiscale decision fusion approach , 2010, Pattern Recognit. Lett..

[7]  Anwar M. Mirza,et al.  Gender recognition from face images with local WLD descriptor , 2012, 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP).

[8]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[9]  Dat Tran,et al.  Classification of gender and face based on gradient faces , 2011, 3rd European Workshop on Visual Information Processing.

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

[11]  Ji Zheng,et al.  A support vector machine classifier with automatic confidence and its application to gender classification , 2011, Neurocomputing.

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

[13]  S. Seker,et al.  Fast gender classification , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.