The face recognition based on ensemble method

Abstract

In this paper, we study the performance of different deep convolutional networks in 1:N face recognition and process the feature comparison in order to obtain a higher face recognition rate. The improved feature comparison method is proposed: using Basis Pursuit De-Noising (BPDN) method combined with Homotopy algorithm to replace the nearest neighbor analysis in 1:N face recognition. Based on the Facenet model, we also compare the performance of different loss functions in face verification and recognition. Furthermore, the ensemble method and voting classifier are used to integrate Facenet, InsightFace and BPDN method to realize 1:N face recognition. It should be noted that the 1:N dataset is transformed from Labeled Faces in the Wild (LFW) dataset, including the 1 dataset and the N dataset. The 1 dataset and the N dataset contain 1680 faces and 5749 faces respectively, one for each person. The final experiments show that the ensemble method's accuracy is 82.98%, which is higher than the above methods, and indicate the ensemble method we proposed can improve the face recognition rate.