Face recognition based on eigen features of multi scaled face components and an artificial neural network

Face recognition has been a very active research area in the past two decades. Many attempts have been made to understand the process of how human beings recognize human faces. It is widely accepted that face recognition may depend on both componential information (such as eyes, mouth and nose) and non-componential/holistic information (the spatial relations between these features), though how these cues should be optimally integrated remains unclear. In the present study, a different observer’s approach is proposed using eigen/fisher features of multi-scaled face components and artificial neural networks. The basic idea of the proposed method is to construct facial feature vector by down-sampling face components such as eyes, nose, mouth and whole face with different resolutions based on significance of face component, and then subspace Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) method is employed for further dimensionality reduction and to acquire a good representation of facial features. Each face in the database to be recognized is projected onto eigenspace or fisherface to find its weight vector. The weight vectors of face images to be trained become the input to a neural network classifier, which uses back propagation/radial basis functions to recognize faces with variation in facial expression, and with/without spectacles. The proposed algorithm has been tested on 400 faces of 10 subjects of the ORL database and results are encouraging compared to the existing methods in literature.