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GPU based Partially Connected Neural Evolutionary network and its application on gender recognition with face images

Xiao-Xi Chen, Ming-Hui Shi, H. de Garis
Dept. of Comput. Sci., Xiamen Univ., Xiamen, China
8th World Congress on Intelligent Control and Automation (WCICA), 2010

@conference{chen2010gpu,

   title={GPU based Partially Connected Neural Evolutionary network and its application on gender recognition with face images},

   author={Chen, X.X. and Shi, M.H. and de Garis, H.},

   booktitle={Intelligent Control and Automation (WCICA), 2010 8th World Congress on},

   pages={1930–1934},

   organization={IEEE}

}

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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 degrees ~40 degrees in the direction of horizontal, and achieved the accuracy rate of 90.84%.
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