The Principal Components Analysis (PCA) is one of the most successfull techniques that have been u sed to recognize faces in images. This technique c onsists of extracting the eigenvectors and eigenvalues of an image from a covariance matrix, which is constructed from an image database. These eigenvectors and eigenvalues are used for image c lassification, obtaining n ice results as far as face recognition is concerned. However, the high computational cost is a major problem of this technique, mainly when real-time applications are involved. There are some evidences that the performance of a PCA-based system that uses only the region around the e yes as input i s very close to a system that uses the whole face. In this case, it is possible to implement faster PCA-based face recognition systems, b ecause only a small region o f the image is considered. This paper reports some results that corroborate this thesis, which have been ob tained within the c ontext of an ongo ing p roject for the development of a performance a ssessment framework for f ace recognition systems. The results of two PCA-based recognition experiments are reported: t he first one c onsiders a more c omplete face region (from the eyebrows to the c hin), while the second is a sub-region o f the first, containing on ly the eyes. The main contributions of the present paper are the description of the performance assessment framework (which is s till under development), the results of the two experiments and a discussion of some possible reasons for them.
['Teófilo Emídio de Campos', 'Rogério Schmidt Feris', 'Roberto Marcondes Cesar Junior']
Eigenfaces Versus Eigeneyes: First Steps Toward Performance Assessment of Representations for Face Recognition