In this paper a novel technique for face recognition is proposed. Using the statistical Local Feature Analysis (LFA) method, a set of feature points is extracted for each face image at locations with highest deviations from the expectation. Each feature point is described by a sequence of local histograms captured from the Gabor responses at different frequencies and orientations around the feature point. Histogram intersection is used to compare the Gabor histogram sequences in order to find the matched feature points between two faces. Recognition is performed based on the average similarity between the best matched points, in the probe face and each of the gallery faces. Several experiments on the FERET set of faces show the superiority of the proposed technique over all considered state-of-the-art methods (Elastic Bunch Graph Matching, LDA+PCA, Bayesian Intra/extrapersonal Classifier, Boosted Haar Classifier), and validate the robustness of our method against facial expression variation and illumination variation.