Face Recognition with Histograms of Oriented Gradients

Abstract

Histograms of Oriented Gradients have been recently used as discriminating features for face recognition. In this work we improve on that work in a number of aspects. As a first contribution, it identifies the necessity of performing feature selection or transformation, especially if HOG features are extracted from overlapping cells. Second, the use of four different face databases allowed us to conclude that, if HOG features are extracted from facial landmarks, the error of landmark localization plays a crucial role in the absolute recognition rates achievable. This implies that the recognition rates can be lower for easier databases if landmark localization is not well adapted to them. This prompted us to extract the features from a regular grid covering the whole image. Overall, these considerations allow to obtain a significant recognition rate increase (up to 10% in some su.bsets) on the standard FERET database with respect to previous work.