GPU based Partially Connected Neural Evolutionary network and its application on gender recognition with face images
Xiaoxi Chen, Minghui Shi, Hugo de GARIS · 2010
An algorithm for evolving neural network via the genetic algorithm based on GPU parallel architecture was implemented on the CUDA, resulting in a system called CuParcone (CUDA based Partially Connected Neural Evolutionary) and was used on gender face recognition. By using the powerful ability of GPU parallel computing, CuParcone achieves a performance increase about 323 times than Parcone algorithm, which runs on a single-processor. With this new model, a gender recognition experiment was made on 530 face images (265 females and 265 males from Color FERET database), including not only frontal faces but also the faces rotated from −40°∼40° in the direction of horizontal, and achieved the accuracy rate of 90.84%.