Locality preserving partial least squares discriminant analysis for face recognition

We propose a locality preserving partial least squares discriminant analysis (LPPLSDA) which adds a locality preserving feature to the conventional partial least squares discriminant analysis(PLS-DA). The locality preserving feature captures the within group structural information via a similarity graph. The ability of LPPLS-DA to capture local structures allows it to be better suited for face recognition. We evaluate the performance of our proposed method on several benchmarked face databases which offer different levels of complexity in terms of sample size as well as image acquisition conditions. The experimental results indicate that, for each database used, the proposed method consistently outperformed the conventional PLS-DA method. (c) 2019 The Author. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).