Abstract—Gender classification is a difficult but also an essential task under the researches of pattern recognition. There are several methods and features used for this task such as face, gait, or full body features. One of the most widely used techniques is Haar cascades. Default Haar features based classifiers can only detect pedestrian, free from gender information. In this paper we aimed to learn the gender of the target pedestrians by Haar cascades that are trained gender specific. We trained the classifier with only male and female images as positive and negative respectively. Once a basic pedestrian detection has been made over whole image, second detection is made in ROI (Region of Interest) which is the first detected rectangle. Even though we implemented this idea for only pedestrians in this step, it can be applied to other binary problems.

Index Terms—Adaboost, gender classification, pattern recognition, pedestrian detection.

The authors are with Department of Electronics Engineering, Kyungsung University, Busan, Korea (email: meyeren2@ks.ac.kr, jsong@ks.ac.kr, jsipark@ks.ac.kr, bwyoon@ks.ac.kr).

Cite:Mustafa E. Yildirim, J. S. Park, J. Song, and B. W. Yoon, "Gender Classification Based on Binary Haar Cascade," International Journal of Computer and Communication Engineering vol. 3, no. 2, pp. 105-108, 2014.