A Gender and Age Recognition Method Based on Deep Learning and Fuzzy Comprehensive Evaluation

Gender and age identification through images, sounds, handwriting, etc., is a very important and active research topic. At present, face features are mainly applied to gender and age prediction in real-time applications. In view of the single factors considered in the current research, a gender and age recognition method based on deep learning and fuzzy comprehensive evaluation is proposed. Firstly, the existing deep learning algorithm is used to predict and recognize the gender and age stage of the experimental images. Secondly, using the principle of anatomy and feature recognition, combined with the method of facial key point recognition, we measure the face of the figure in the image. Finally, based on the fuzzy evaluation theory, the two conclusions are given corresponding weights, and the final gender and age stage results are obtained.. The recognition accuracy of gender and age stages on the Adience public dataset was 99.32% and 97.17%, which was 0.27% and 1.32% higher than the recognition accuracy obtained by using only the deep learning recognition method.