A gender recognition system using shunting inhibitory convolutional neural networks

In this paper, we employ shunting inhibitory convolutional neural networks to develop an automatic gender recognition system. The system comprises two modules: a face detector and a gender classifier. The human faces are first detected and localized in the input image. Each detected face is then passed to the gender classifier to determine whether it is a male or female. Both the face detection and gender classification modules employ the same neural network architecture; however, the two modules are trained separately to extract different features for face detection and gender classification. Tested on two different databases, Web and BioID database, the face detector has an average detection accuracy of 97.9%. The gender classifier, on the other hand, achieves 97.2% classification accuracy on the FERET database. The combined system achieves a recognition rate of 85.7% when tested on a large set of digital images collected from the Web and BioID face databases.