(Multiscale) Local Phase Quantisation histogram discriminant analysis with score normalisation for robust face recognition

摘要

In video based face recognition, faces typically experience challenging illumination conditions, blur, or localisation errors in several frames. To alleviate these challenges, quality measures can be used to remove the most severely degraded frames. Still, when the videos are taken in real life settings, degradations are likely to be present even in the highest quality frames, and robust recognition techniques are required. In this paper, a novel discriminative face representation derived by the Linear Discriminant Analysis of (Multiscale) Local Phase Quantisation (LPQ) histogram is proposed. First, a (multiscale) LPQ operator is applied to the face image. Next, histograms are extracted from local regions of resultant images, and projected into an LDA space to form a discriminative regional face descriptor. These methods are implemented and tested on the problem of video based face identification using the BANCA video database. Additionally, to verify their performance, experiments on standard still image FERET and BANCA face databases showing very promising results are reported.