DocumentCode
1691581
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
fYear
2010
Firstpage
1930
Lastpage
1934
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location
Jinan
Print_ISBN
978-1-4244-6712-9
Type
conf
DOI
10.1109/WCICA.2010.5554600
Filename
5554600
Link To Document