Gender recognition based on multiple scale textural feature

Traditional gender recognition technologies with single feature cannot express an image completely and are limited by their recognition speed and accuracy. In this paper, we explored a way of fulfilling this task by combing the characteristics of both Haar-like and textural feature and proposed the approach to construct a multiple scale textural feature (MST), meanwhile, in order to achieve heigh recognition accuracy, we further improved the Adaboost algorithm improved by Freidman et al. Data from experiments based on MIT database show that our MST feature working together with the improved Adaboost algorithm can obtain a recognition rate of 86%.