Eigenfaces Versus Eigeneyes: First Steps Toward Performance Assessment of Representations for Face Recognition

The Principal Components Analysis (PCA) is one of the most successfull techniques that have been used to recognize faces in images. This technique consists 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 classification, obtaining nice 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 eyes as input is very close to a system that uses the whole face. In this case, it is possible to implement faster PCA-based face recognition systems, because only a small region of the image is considered. This paper reports some results that corroborate this thesis, which have been obtained within the context of an ongoing project for the development of a performance assessment framework for face recognition systems. The results of two PCA-based recognition experiments are reported: the first one considers a more complete face region (from the eyebrows to the chin), while the second is a sub-region of the first, containing only the eyes. The main contributions of the present paper are the description of the performance assessment framework (which is still under development), the results of the two experiments and a discussion of some possible reasons for them.