Gender recognition by face is one of the actual problems of computer vision. Gender recognition could be useful in the number of applications for biometric authentication, hightech surveillance and security systems, criminology, automatic psychophysiologic inspection, augmented reality etc. Also the applicability of gender recognition is growing in a such areas as social science, statistics and marketing research. Also there are a lot of applications (especially in social nets) based on different face recognition algorithms (including sex classification) for entertainment of users. Thats why sex recognition by face is of interest by computer vision scientists during last two decades.
Gender recognition can be regarded as classification problem of detected faces into classes (males & females). The gender recognition task is being investigated from the beginning of 90-th of XX-th century. The best of the state-of-the-art results reported in scientific papers are about 95% accuracy. After testing of several the most promising approaches we succeeded in achieving 96% for male and 95% for female faces on FERET face image database. It was achieved on LBP (Local Binary Patterns) features classified by SVM (Support Vector Machine) with RBF (Radial Basis Function) kernel function.
Gender recognition can be regarded as classification problem of detected faces into classes (males & females). The gender recognition task is being investigated from the beginning of 90-th of XX-th century. The best of the state-of-the-art results reported in scientific papers are about 95% accuracy. After testing of several the most promising approaches we succeeded in achieving 96% for male and 95% for female faces on FERET face image database. It was achieved on LBP (Local Binary Patterns) features classified by SVM (Support Vector Machine) with RBF (Radial Basis Function) kernel function.
