Universiti Teknologi Malaysia Institutional Repository

Gender classication: a convolutional neural network approach

Liew, Shan Sung and Mohd. Hani, Mohamed Khalil and Ahmad Radzi, Syafeeza and Bakhteri, Rabia (2016) Gender classication: a convolutional neural network approach. Turkish Journal of Electrical Engineering & Computer Sciences, 24 . pp. 1248-1264. ISSN 1300-0632

Full text not available from this repository.

Official URL: http://dx.doi.org/10.3906/elk-1311-58

Abstract

An approach using a convolutional neural network (CNN) is proposed for real-time gender classication based on facial images. The proposed CNN architecture exhibits a much reduced design complexity when compared with other CNN solutions applied in pattern recognition. The number of processing layers in the CNN is reduced to only four by fusing the convolutional and subsampling layers. Unlike in conventional CNNs, we replace the convolution operation with cross-correlation, hence reducing the computational load. The network is trained using a second-order backpropagation learning algorithm with annealed global learning rates. Performance evaluation of the proposed CNN solution is conducted on two publicly available face databases of SUMS and AT&T. We achieve classication accuracies of 98.75% and 99.38% on the SUMS and AT&T databases, respectively. The neural network is able to process and classify a 32 32 pixel face image in less than 0.27 ms, which corresponds to a very high throughput of over 3700 images per second. Training converges within less than 20 epochs. These results correspond to a superior classication performance, verifying that the proposed CNN is an effective real-time solution for gender recognition.

Item Type:Article
Additional Information:RADIS System Ref No:PB/2016/06205
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:68390
Deposited By: Haliza Zainal
Deposited On:02 Nov 2017 02:59
Last Modified:20 Nov 2017 08:52

Repository Staff Only: item control page