DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

Abstract

In this paper, we propose a robust face recognition model called DeepWTPCA-<inline-formula> <tex-math notation= LaTeX >$L_{1}$ </tex-math></inline-formula> using WTPCA-<inline-formula> <tex-math notation= LaTeX >$L_{1}$ </tex-math></inline-formula> features and a CNN-LSTM architecture. First, WTPCA-<inline-formula> <tex-math notation= LaTeX >$L_{1}$ </tex-math></inline-formula> algorithm, composed of Three-level decomposition of discrete wavelet transform followed by PCA-<inline-formula> <tex-math notation= LaTeX >$L_{1}$ </tex-math></inline-formula> algorithm, is exploited to extract face features. Then, the extracted features are used as inputs of the proposed CNN-LSTM architecture. To evaluate the robustness of the proposed approach, several face recognition datasets have been used. In addition, the proposed method is trained on noisy images using Gaussian, and Salt & Pepper noise added to the facial images of each dataset. The results of the experiment indicate that the proposed model achieves high recognition performance on three well-known standard face databases. When compared to state-of-the-art methods, the proposed model achieves a better face recognition rate.