TY - GEN
T1 - A shunting inhibitory convolutional neural network for gender classification
AU - Tivive, Fok Hing Chi
AU - Bouzerdoum, Abdesselam
PY - 2006
Y1 - 2006
N2 - Demographic features, such as gender, are very important for human recognition and can be used to enhance social and biometric applications. In this paper, we propose to use a class of convolutional neural networks for gender classification. These networks are built upon the concepts of local receptive field processing and weight sharing, which makes them more tolerant to distortions and variations in two dimensional shapes. Tested on two separate data sets, the proposed networks achieve better classification accuracy than the conventional feedforward multilayer perceptron networks. On the Feret benchmark dataset, the proposed convolutional neural networks achieve a classification rate of 97.1%.
AB - Demographic features, such as gender, are very important for human recognition and can be used to enhance social and biometric applications. In this paper, we propose to use a class of convolutional neural networks for gender classification. These networks are built upon the concepts of local receptive field processing and weight sharing, which makes them more tolerant to distortions and variations in two dimensional shapes. Tested on two separate data sets, the proposed networks achieve better classification accuracy than the conventional feedforward multilayer perceptron networks. On the Feret benchmark dataset, the proposed convolutional neural networks achieve a classification rate of 97.1%.
UR - https://www.scopus.com/pages/publications/34147162186
U2 - 10.1109/ICPR.2006.173
DO - 10.1109/ICPR.2006.173
M3 - Conference contribution
AN - SCOPUS:34147162186
SN - 9780769525211
T3 - Proceedings - International Conference on Pattern Recognition
SP - 421
EP - 424
BT - Track D
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th International Conference on Pattern Recognition, ICPR 2006
Y2 - 20 August 2006 through 24 August 2006
ER -