Auto-Associative Neural Networks and Eigenbands Fusion for Frontal Face Verification

Face classification is an important area of research with many applications, including biometric security and searching face databases. This article describes an approach to verify faces using Auto-associative Neural Networks and Eigenbands fusion. In Eigenbands strategy each faces is divided in horizontal bands from which are extracted features using PCA. This method aims capture discriminative facial features. Results showed that the Eigenbands Fusion with Auto-associative Neural Networks found goods rates to the problem of face verification and it was more accurate than the traditional approach of PCA.