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 developm...

• Type: Conference
• Year: 2000
• Where: MICAI
• Authors: Teófilo Emídio de Campos, Rogério Schmidt Feris, Roberto Marcondes Cesar Junior
• Keywords: Artificial Intelligence, Face Recognition, Face Recognition Systems, MICAI 2000, Performance Assessment Framework