Face Image Feature Extraction based on Deep Learning Algorithm

Abstract

In recent years, due to the rapid development of computer technology, artificial intelligence technology in the computer field has begun to integrate into people’s life, and facial recognition, as a unique biometric recognition method, is the core of artificial intelligence technology. Based on this, this paper discusses the local feature extraction and global feature extraction based on the deep learning algorithm, and proposes a training classification method based on the deep learning model combined with local pattern and GLQP representation feature extraction algorithm. In this paper, the local quantization method is used to input the data set preprocessed by the filter into the network. The depth of CNN network is selected as 4 layers, and the network is trained to produce high-resolution features. Experiments show that the accuracy of the trained deep network model is 92.2% in the test set. Therefore, compared with the traditional methods, deep learning has the advantages of powerful visualization and automatic face feature extraction, overcomes the shortcomings of deep learning model in the process of shallow feature learning, and shows higher recognition efficiency and generalization.

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