In the last ten years, many variants of the principal component analysis were suggested to fight against the curse of dimensionality. Recently, A. Sharma et al. have proposed a stable numerical algorithm based on Householder QR decomposition (HQR) called QR PCA. This approach improves the performance of the PCA algorithm via a singular value decomposition (SVD) in terms of computation complexity. In this paper, we propose a new algorithm called RRQR PCA in order to enhance the QR PCA performance by exploiting the Rank-Revealing QR Factorization (RRQR). We have also improved the recognition rate of RRQR PCA by developing a nonlinear extension of RRQR PCA. In addition, a new robust RBF Lp-norm kernel is proposed in order to reduce the effect of outliers and noises. Extensive experiments on two well-known standard face databases which are ORL and FERET prove that the proposed algorithm is more robust than conventional PCA, 2DPCA, PCA-L1, WTPCA-L1, LDA, and 2DLDA in terms of face recognition accuracy.

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In this paper, we propose a robust face recognition model called DeepWTPCA-L 1 using WTPCA-L 1 features and a CNN-LSTM architecture. First, WTPCA-L 1 algorithm, composed of Threelevel decomposition of discrete wavelet transform followed by PCA-L 1 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-ofthe-art methods, the proposed model achieves a better face recognition rate.

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