Subspace methods for face recognition

Studying the inherently high-dimensional nature of the data in a lower dimensional manifold has become common in recent years. This is generally known as dimensionality reduction. A very interesting strategy for dimensionality reduction is what is known as subspace analysis. Beginning with the Eigenface method, face recognition and in general computer vision has witnessed a growing interest in algorithms that capitalize on this idea and an ample number of such efficient algorithms have been proposed. These algorithms mainly differ in the kind of projection method used (linear or non-linear) or in the criterion employed for classification. The objective of this paper is to provide a comprehensive performance evaluation of about twenty five different subspace algorithms under several important real time test conditions. For this purpose, we have considered the performance of these algorithms on data taken from four standard face and object databases namely ORL, Yale, FERET and the COIL-20 object database. This paper also presents some theoretical aspects of the algorithm and the analysis of the simulations carried out.