Age and gender classification using wide convolutional neural network and Gabor filter - Hosseini, Sepidehsadat; Lee, Seok Hee; Kwon, Hyuk Jin; Koo, Hyung Il; Cho, Nam Ik (2018 International Workshop on Advanced Image Technology, IWAIT 2018, 2018)
Age and gender classification has received more attention recently owing to its important role in user-friendly intelligent systems. In this paper, we propose a convolutional neural network (CNN) based architecture for joint age-gender classification, where we use the Gabor filter responses as the input. The weighting of Gabor-filter responses is learned through back-propagation in an end-to-end architecture. The architecture is trained to label the input images into 8 ranges of age and 2 types of gender. Our approach shows improved accuracy in both age and gender classification compared to the state-of-the-art methodologies. We also observe that increasing the width of neural network would increase the accuracy of the overall system.
Keywords: Convolutional neural network; Convolutional Neural Networks (CNN); End to end; Filter response; Gender classification; Input image; State of the art; User friendly; Classification; Convolutional neural network; Deep learning; Gabor filter