Local feature analysis for robust face recognition

In this paper a novel technique for face recognition is proposed. Using the statistical local feature analysis (LFA) technique, a set of feature points is extracted from each face image, at locations with highest deviations from the statistical expected face. Each feature point is described by a set of Gabor wavelet responses at different frequencies and orientations. A triangle-inequality-based pruning algorithm is developed for fast matching, which automatically chooses a set of key features from the database of model features and uses the pre-computed distances of the keys to the database, along with the triangle inequality, in order to speedily compute lower bounds on the distances from a query feature to the database, and eliminate the unnecessary direct comparisons. Our proposed technique achieves perfect results on the ORL face set and an accuracy rate of 99.1% on the FERET face set, which shows the superiority of the proposed technique over all considered state-of-the-art face recognition methods.