Gender Classification Based on Binary Haar Cascade

The present invention relates to a gender classification method based on a binary haar cascade. More particularly, the gender classification method based on a binary haar cascade detects a pedestrian region from a screen image after learning the features of women such as female images, makeup, hair styles, ear rings, or glasses types, determines whether there are the features of women from the pedestrian region, and distinguishes the sex of pedestrians by displaying detection for the heads of the pedestrians in the pedestrian region of the screen image when the features of the women exist.

[1]  Angel D. Sappa,et al.  Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection , 2007 .

[2]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[3]  Cunlu Xu,et al.  Gender recognition based on multiple scale textural feature , 2012, 2012 5th International Congress on Image and Signal Processing.

[4]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[5]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[6]  N.D. Georganas,et al.  Real-time Vision-based Hand Gesture Recognition Using Haar-like Features , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[7]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[8]  G. R. Rakate,et al.  Advanced Pedestrian Detection system using combination of Haar-like features, Adaboost algorithm and Edgelet-Shapelet , 2012, 2012 IEEE International Conference on Computational Intelligence and Computing Research.