Title :
GPU based Partially Connected Neural Evolutionary network and its application on gender recognition with face images
Author :
Chen, Xiao-Xi ; Shi, Ming-Hui ; De Garis, Hugo
Author_Institution :
Dept. of Comput. Sci., Xiamen Univ., Xiamen, China
Abstract :
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%.
Keywords :
computer graphics; face recognition; neural nets; parallel architectures; CuParcone; GPU; Parcone algorithm; face images; face recognition; gender recognition; parallel architecture; partially connected neural evolutionary network; Computational modeling; Face; Face recognition; Graphics processing unit; Image color analysis; Image recognition; Support vector machines; CUDA; gender recognition; neural networks; parallel computing;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
DOI :
10.1109/WCICA.2010.5554600