SH-CNN: Shearlet Convolutional Neural Network for Gender Classification
Chaymae Ziani, Abdelalim Sadiq · Advances in Science Technology and Engineering Systems Journal · 2020
Gender detection and age estimation become an active research area and a very important field today, wish has been widely used in various applications including them: biometrics, social network, Targeted advertising, access control, human-computer interaction, electronic customer, etc.The need to further improve the recognition or classification rate keeps increasing day after day.In this paper, we explore how deep learning techniques can help in the classification of gender from human face images and moreover raise the recognition rate.We propose in this contribution an approach called SH-CNN based on Discrete Shearlet Transform (DST) as a first step of feature extraction layer, and Deep Convolutional Neural Network (DCNN) as a second automatic feature extraction layer and also a classification step.The idea behind our contribution is to generate trough DST several features (in different decomposition and orientation) of an image.These features of each image will be the input of the DCNN, to enrich the training step, and so, improve the recognition rate.The obtained results have shown that the proposed approach import a significant enhancement.