Frontal view recognition using spectral clustering and subspace learning methods

Abstract

In this paper, the problem of frontal view recognition on still images is confronted, using subspace learning methods. The aim is to acquire the frontal images of a person in order to achieve better results in later face or facial expression recognition. For this purpose, we utilize a relatively new subspace learning technique, Clustering based Discriminant Analysis (CDA) against two well-known in the literature subspace learning techniques for dimensionality reduction, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). We also concisely describe spectral clustering which is proposed in this work as a preprocessing step to the CDA algorithm. As classifiers, we use the K-Nearest Neighbor the Nearest Centroid and the novel Nearest Cluster Centroid classifiers. Experiments conducted on the XM2VTS database, demonstrate that PCA+CDA outperforms PCA, LDA and PCA+LDA in Cross Validation inside the database. Finally the behavior of these algorithms, when the size of training set decreases, is explored to demonstrate their robustness.