Comparing SIFT and LDA based face recognition approaches

Abstract

In this paper we present a face recognition system based on the Scale Invariant Feature Transform (SIFT) image descriptors recently proposed by Lowe [6] and largely used in generic object recognition tasks. We show how SIFT descriptors can be used in a robust face recognition system coupled with some simple image normalization processes and geometric constraints on SIFT matchings. In the paper we present an extensive experimental evaluation of the prosed SIFT-based face recognition approach comparing it with a “standard” Linear Discriminant Analysis (LDA)-based method, commonly considered one of the best performing face recognition technique. The two systems have been tested using images collected from different face recognition benchmarks in order to simulate real-life applications in which image acquisition parameters largely vary from one query image to the other. In all our tests the SIFT-based method clearly outperformed the LDA-based one, showing that the a priori knowledge embedded in the SIFT local description of image appearances is more robust than trainingbased systems in which appearance variability factors need to be off-line learned.