Face recognition method based on fusion of improved MobileFaceNet and adaptive Gamma algorithm

MobileFaceNet face recognition algorithm is a relatively mainstream face recognition algorithm at present. Its advantages of small memory and fast running speed make it widely used in embedded devices. Due to the limited face image acquisition capability of embedded devices, the accuracy of face recognition is often reduced due to uneven illumination and poor exposure quality. In order to solve this problem, a face recognition algorithm based on the fusion of MobileFaceNet and adaptive Gamma algorithm is proposed. The application of the algorithm proposed in this paper in image preprocessing is as follows. Firstly, adaptive Gamma correction is used to improve the brightness of the face image. Then, the edge of the face image is enhanced by the Laplace operator. Finally, a linear weighted fusion was performed between the Gamma corrected image and the enhanced edge image to obtain the pre-processed face image. At the same time, we have improved the traditional MobileFaceNet network. The feature extraction network MobileFaceNet has been improved by adding a Stylebased Recall Module (SRM) attention mechanism to its bottom neck layer, utilizing the mean and standard deviation of input features to improve the ability to capture global information and enhance more important feature information. Finally, the proposed method was verified on the LFW and Agedb face test set. The experimental results show that the adaptive Gamma algorithm proposed in this paper and the improvement of MobileFaceNet can achieve a face recognition accuracy of 99.27 % on LFW dataset and 90.18 % on Agedb dataset while only increasing the model size by 0.4 M and the processing speed for each image is enhanced by 4 ms. which can effectively improve the accuracy of face recognition and better application prospects on embedded devices. The method presented in this article has certain practical significance.