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Several methods and features have been proposed for gender recognition problem. Histogram of oriented gradients (Hog) is a widely used feature in image processing. This study proposes a gender recognition method using full body features. Human body from side and front view were represented by Hog. Using all bins in the histogram requires longer time for training. In order to decrease the computation time, descriptor size should be decreased. Inter-class difference was obtained as a vector and sorted in a descending order. The bins with the largest value were selected among this vector. Random forest and Adaboost methods were used for the recognition. As a result of both tests, the classifier using first 100 bins with maximum difference gives the optimum performance in terms of accuracy rate and computation time. Although Adaboost performed faster, the accuracy of random forest is higher in full body gender recognition.

Gender Recognition using Hog with Maximized Inter-Class Difference