Face Recognition Algorithm Based on VGG Network Model and SVM

The problem that the dimension of facial features is too large does exist with the Deep learning face recognition. This paper proposes a face recognition algorithm based on SVM combined with VGG network model extracting facial features, which can not only accurately extract face features, but also reduce feature dimensions and avoid irrelevant features to participate in the calculation. Firstly, the VGG-16 model is obtained by training the training data set, which is used for feature extraction, on top of this, principal component analysis method (PCA) is used for feature dimensionality reduction, and last, the face recognition is performed by SVM classifier with linear kernel function. In this paper, we conduct a comparative experiment on CelebA dataset and find that the accuracy reaches its peak when the feature dimension is reduced to 400.The experiment is carried out on LFW dataset using 400-dimensional feature data, and comparing with other algorithms, the results show that the algorithm in this paper has reached the level of state-of-art.