Gender Recognition with Face Images Based on Partially Connected Neural Evolutionary

In this paper, a new type of neural network model Partially Connected Neural Evolutionary (PARCONE) was introduced to recognize a face gender. The neural network has a mesh structure in which each neuron didn't connect to all other neurons but maintain a fixed number of connections with other neurons. In training, the evolutionary computation method was used to improve the neural network performance by change the connection neurons and its connection weights. With this new model, no feature extraction is needed and all of the pixels of a sample image can be used as the inputs of the neural network. The gender recognition experiment was made on 490 face images (245 females and 245 males from Color FERET database), which include not only frontal faces but also the faces rotated from -- 40°- 40° in the direction of horizontal. After 300 - 600 generations' evolution, the gender recognition rate, rejection rate and error rate of the positive examples respectively are 96.2%, 1.1%, and 2.7%. Furthermore, a large-scale GPU parallel computing method was used to accelerate neural network training. The experimental results show that the new neural model has a better pattern recognition ability and may be applied to many other pattern recognitions which need a large amount of input information.