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Comparison of Face Recognition and Detection Models: Using Different Convolution Neural Networks

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Abstract

Face detection and recognition plays an important role in many occasions. This study explored the application of convolutional neural network in face detection and recognition. Firstly, convolutional neural network was briefly analyzed, and then a face detection model including three convolution layers, four pooling layers, introduction layers and three fully connected layers was designed. In face recognition, the self-learning convolutional neural network (CNN) model for global and local extended learning and Spatial Pyramid Pooling (SPP)-NET model were established. LFW data sets were used as model test samples. The results showed that the face detection model had an accuracy rate of 99%. In face recognition, the self-learning CNN model had an accuracy rate of 94.9% accuracy, and the SPP-Net model had an accuracy rate of 92.85%. It suggests that the face detection and recognition model based on convolutional neural network has good accuracy, and the face recognition efficiency of self-learning CNN model was better, which deserves further research and promotion.

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Authors and Affiliations

  1. College of Information Science and Technology and PRT Advanced Printing Technology Innovation Laboratory, Xiamen University Tan Kah Kee College, 363105, Fujian, Zhangzhou, China

    Kai Kang

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  1. Kai Kang
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Correspondence to Kai Kang.

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Kai Kang Comparison of Face Recognition and Detection Models: Using Different Convolution Neural Networks. Opt. Mem. Neural Networks 28, 101–108 (2019). https://doi.org/10.3103/S1060992X19020036

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