An efficient face recognition approach combining likelihood-based sufficient dimension reduction and LDA

Abstract

In this paper, an efficient approach is proposed for face recognition (FR) under pose and illumination variations. It is based on combining likelihood-based sufficient dimension reduction (LSDR) and linear discriminant analysis (LDA) using different facial features. LDA is a well–established technique for dimensionality reduction, while LSDR is a relatively new supervised subspace learning method based on the key concept of sufficiency, and consists of estimating a central subspace for low-dimensional representation of facial images. We show, empirically, that this combination can obviously increase the separability between face classes. The facial features that were considered here are either engineered (hand-crafted) features (e.g., discrete shearlet transform coefficients) or learned features which are obtained by retraining a deep learning-based system called FaceNet. To assess the performance of the proposed approach, different classification methods were used during the evaluation phase, namely collaborative representation based classifier (CRC), KNN, linear SVM and three regression-based classifiers (LSRC, LRC and LDRC). The extensive experiments performed on four publicly available face databases – the extended Yale B, FERET, LFW and CMU Multi-PIE– have demonstrated that, on the one hand, LSDR is an effective and efficiency dimensionality reduction technique for face recognition. Particularly, it can be used to significantly increase the performance of a deep learning-based system such as FaceNet, mainly, when training samples are insufficient. On the other hand, its combination with LDA can outperform best individual face recognition algorithm based on LSDR or LDA. Furthermore, the proposed approach compares favorably to current state-of-the-art methods on face recognition, in terms of classification accuracy, under illumination and pose variations.