%0 Journal Article
%T A Real-Time Angle- and Illumination-Aware Face Recognition System Based on Artificial Neural Network
%A Hisateru Kato
%A Goutam Chakraborty
%A Basabi Chakraborty
%J Applied Computational Intelligence and Soft Computing
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/274617
%X Automatic authentication systems, using biometric technology, are becoming increasingly important with the increased need for person verification in our daily life. A few years back, fingerprint verification was done only in criminal investigations. Now fingerprints and face images are widely used in bank tellers, airports, and building entrances. Face images are easy to obtain, but successful recognition depends on proper orientation and illumination of the image, compared to the one taken at registration time. Facial features heavily change with illumination and orientation angle, leading to increased false rejection as well as false acceptance. Registering face images for all possible angles and illumination is impossible. In this work, we proposed a memory efficient way to register (store) multiple angle and changing illumination face image data, and a computationally efficient authentication technique, using multilayer perceptron (MLP). Though MLP is trained using a few registered images with different orientation, due to generalization property of MLP, interpolation of features for intermediate orientation angles was possible. The algorithm is further extended to include illumination robust authentication system. Results of extensive experiments verify the effectiveness of the proposed algorithm. 1. Introduction The need for personal identification has grown enormously in the last two decades. Previously, biometric identification using fingerprints or face images was restricted to criminal prosecution only. A few experts could serve the demand. With increased terrorist activities, stricter security requirements for entering buildings, and other related applications, need for automatic biometric machine-authentication systems is getting more and more important. Recognizing people from face (face image) is the most natural and widely used method we human do always and effortlessly. Due to ease of collection without disturbing the subject, it is one of the most popular ways of automatic machine authentication. An excellent survey of face-recognition algorithms is available in [1]. In automatic face recognition, the first step is to identify the boundary of the face and separate it from the photographed image. Next, recognition algorithms extract feature vectors from the input (probe) image. These features are then compared with the set of such features stored in the database. The database (gallery image) contains same set of features already extracted and stored during registration phase for all persons required to be authenticated. There are two
%U http://www.hindawi.com/journals/acisc/2012/274617/