Research on Intelligent Classification Algorithm of Human Faces Based on Deep Learning

Abstract

Traditional face classification algorithm has low accuracy for gender classification. Combined with the characteristics of deep feature extraction of convolutional neural network in deep learning, a face intelligent classification model based on Inception-ResNet network and estimated LogistiC regression model is constructed by stacking generalization integration method. In this model, Inception-ResNet network is adopted as level 0 learner, and binomial Logistic regression model is used as levell learners. In this way, deep learning and intelligent classification of face images are carried out. Experimental results show that the gender classification prediction accuracy of the proposed Inception-ResNet network is as high as 97.45 ± 0.78, which is higher than that of single VGG16 and ResNet50 network models. Compared with the other two face intelligent classification algorithms, the classification accuracy of the proposed algorithm is 5.52% and 4.69% higher than that of the other two algorithms, respectively. Therefore, the proposed algorithm can achieve accurate gender classification through face recognition, and the classification accuracy is high, which can further accelerate the application of intelligent technology.