Face Recognition Based on Deep Learning Under the Background of Big Data
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.
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