Title: Incremental robust principal component analysis for face recognition using ridge regression

Authors: Haïfa Nakouri; Mohamed Limam

Addresses: Institut Supérieur de Gestion, LARODEC, Université de Tunis, Tunisia; Faculté des Sciences Juridiques, Economiques et de Gestion de Jendouba, Université de Jendouba, Tunisia ' Dhofar University, Thumrayt St., Salalah, Oman

Abstract: Face recognition efficiency is extremely challenged by image corruption, noise, shadowing and variant face expressions. In this paper, we propose a reliable incremental face recognition algorithm to address this problem. The algorithm is robust to face image misalignment, occlusion, corruption and different style variations. To apply this for large face data streams, the proposed algorithm uses incremental robust principal component analysis (PCA) to regain the intrinsic data of a bunch of images regarding one subject. A novel similarity metric is defined for face recognition and classification. Five different databases and a base of four different criteria are used in the experiments to illustrate the reliability of the proposed method. Experiments point that it outperforms other existing incremental PCA approaches namely incremental singular value decomposition, add block singular value decomposition and candid covariance-free incremental PCA in terms of recognition rate under occlusions, facial expressions and image perspectives.

Keywords: image alignment; robust principal component analysis; RPCA; incremental RPCA; ridge regression.

DOI: 10.1504/IJBM.2017.10007740

International Journal of Biometrics, 2017 Vol.9 No.3, pp.186 - 204

Received: 08 Apr 2016
Accepted: 30 Apr 2017

Published online: 15 Sep 2017 *

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