Design and implementation of the algorithm for gender and face classification of face images based on convolutional neural network

Abstract

Convolutional Neural Network (CNN) is a feedforward neural network suitable for large-scale image processing. CNN has been largely developed these years, it is primarily used to identify displacement, and it uses training to extract features from raw data to gain learning and judgment. Age and gender classification of face images is a kind of biometric technology that identifies people by extracting their biological features. This paper combines the convolutional neural network with the classification and recognition algorithms for the gender and age of the human face, uses the Adience dataset to train the network so as to realize the function of judging gender and estimating age on the Internet. With achieved AlexNet architecture using TensorFlow, the CNN initially completed the expected goals, and analyzed and evaluated the factors that affect network performance, and discussed the improvement methods. Using the face database to train the convolutional neural network, the network finally grasps the ability of face recognition of gender and age. The main problems to be solved are: the training of convolutional neural network data sets and the adjustment of parameters; the extraction and processing of information on face images that need to be identified; the application of convolutional neural networks to face recognition and age estimation. Specifically, it includes: constructing a reasonable convolutional neural network architecture; correctly inputting data and outputting corresponding results; processing and extracting effective information of the pictures.