Gender Recognition Based On Face Images

Face is an important biological feature. Face images contain a great deal of information, such as gender, age, race, ID, etc. Gender recognition is an attempt to give the computers the ability to discriminate the gender information from a face image.Generally speaking, one gender classification system consists of three modules, face image preprocessing, facial feature extraction and classifier. This paper researches on the three modules and compares some different methods.In this paper, a novel gender classification method based on frontal face images is presented. In this work, the global features are extracted using AdaBoost algorithm. Active Appearance Model (AAM) locates 83 landmarks, from which the local features are characterized. After the fusion of the local and global features, the mixed features are used to train support vector machine (SVM) classifiers. This method is evaluated by the recognition rates over a mixed face database containing over 21,300 images from 4 sources (AR, FERET, CAS-PEAL-R1, WWW and a database collected by the lab). Experimental results show that the hybrid method outperforms the unmixed appearance- or geometry-feature based methods and achieve a classification rate over 90%. Reasonable suggestions on the extraction of facial region and the selection of AdaBoost structure is given based on carefully designed experiments.