Fusion of local normalization and Gabor entropy weighted features for face identification

Face recognition is one of the most extensively studied topics in image analysis because of its wide range of possible applications such as in surveillance, access control, content-based video search, human-computer interaction, electronic advertisement and more. Face identification is a one-to-n matching problem where a captured face is compared to n samples in a database. In this work we propose two new methods for face identification. The first one combines entropy-like weighted Gabor features with the local normalization of Gabor features. The second fuses the entropy-like weighted Gabor features at the score level with the local binary pattern (LBP) applied to the magnitude (LGBP) and phase (LGXP) components of the Gabor features. We used the FERET, AR, and FRGC 2.0 databases to test and compare our results with those previously published. Results on these databases show significant improvement relative to previously published results, reaching the best performance on the FERET and AR databases. Our methods also showed significant robustness to slight pose variations. We tested the proposed methods assuming noisy eye detection to check their robustness to inexact face alignment. Results show that the proposed methods are robust to errors of up to 3 pixels in eye detection. HighlightsWe propose an entropy weighted strategy over selected Gabor features.We fuse local normalization with Gabor features to improve face identification.Fusion of different features improve face identification relative to cases with no fusion.Our results are compared advantageously on several international face databases.We tested our methods with variable illumination, gesticulation, pose and occlusion.