Abstract
Face recognition has important value in real life. In this study, the application of the deep learning method in the field of face recognition was studied. The structure of LeNet-5 in convolutional neural network (CNN) was selected and improved; based on it, a face recognition method was designed. The performance of the method was analyzed taking CelebA as training set and LEW as testing set. The results showed that the improved LeNet-5 model which took A-softmax Loss as loss function not only had shorter training time, but also had higher recognition accuracy, its accuracy increased with the increase of sample size, and the highest accuracy rate reached 97.9%. The experimental results showed that the face recognition method designed in this study had good performance in large data background as it could effectively reduce the running time of the algorithm and improve the recognition accuracy. This study proves the reliability of deep learning methods such as CNN in face recognition, which is conducive to the further development of face recognition technology.
Highlights
With the development of computer technology and in the context of big data, people pay more attention to issues such as data security and personal privacy, and the social requirements for human identification are increasing
The reliability of the method was proved by LFW data set, which provides some theoretical support for the further application of deep learning in face recognition
Taking A-softmax Loss as the loss function, two convolutional neural network (CNN) models were tested using LFW data sets. 100 pairs, 500 pairs, 1000 pairs and 2000 pairs of matched face images were taken as positive samples; as shown in Figure 5, the two images matched each other, which was called a pair of positive samples
Summary
With the development of computer technology and in the context of big data, people pay more attention to issues such as data security and personal privacy, and the social requirements for human identification are increasing. The deep learning method has excellent performance in face recognition, especially in big data processing [3], and relevant research is deepening. Based on the deep neural network, an antinoise network was designed, and the reliability of the network in face recognition with noise was proved by experiments. Lu et al [5] proposed a deeply coupled ResNet model, which was composed of a relay network and two branch networks It could extract various possible resolutions of images, and the reality of the model was proved by experiments in LFW and SCface databases. The reliability of the method was proved by LFW data set, which provides some theoretical support for the further application of deep learning in face recognition
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