Wavelet-based convolutional neural networks for gender classification.

We develop a gender classification method using convolutional neural networks. We train Alexnet Architecture using the luminance (Y) component of the facial image (YCbCr) for the SoF, groups, and face recognition technology datasets. The Y component is reduced to a size of 32 x 32 via discrete wavelet transform (DWT). The use of the Y plane and a low-resolution subband image of the DWT significantly reduce the amount of processed data. We are able to achieve better results than other machine learning, rule-based approaches and the traditional convolutional neural net structure that are trained with three-dimensional RGB images. We are able to maintain comparably high recognition accuracy, even with the reduction of the number of network layers. We have also compared our structure with the state-of-the-art methods and provided the recognition rates. (C)2019 SPIE and IS&T