Advances in face recognition have come from considering various aspects of this specialized perception problem. Earlier methods treated face recognition as a standard pattern recognition problem; later methods focused more on the representation aspect, after realizing its uniqueness using domain knowledge; more recent methods have been concerned with both representation and recognition, so a robust system with good generalization capability can be built by adopting state-of-the-art techniques from learning, computer vision, and pattern recognition. A face recognition system based on recent method which concerned with both representation and recognition using artificial neural networks is presented. This paper initially provides the overview of the proposed face recognition system, and explains the methodology used. It then evaluates the performance of the system by applying two (2) photometric normalization techniques: histogram equalization and homomorphic filtering, and comparing with Euclidean Distance, and Normalized Correlation classifiers. The system produces promising results for face verification and face recognition.

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A face recognition system based on recent method which concerned with both representation and recognition using learning algorithm is presented. The learning algorithm, artificial neural network is used as a classifier for face recognition and face verification whereas the features are extracted using linear subspace techniques. This paper initially provides the overview of the proposed face recognition system, and explains the methodology used. It then explains the performance evaluation of the proposed system by applying two photometric normalization techniques: Histogram equalization and Homomorphic filtering, and comparing with Euclidean Distance and Normalized Correlation classifiers. The system produces promising results for face verification and face recognition where it achieved False Acceptance Rate (FAR) of 2.98% and False Rejection Rate (FRR) of 2.59% using ANN classifier with PCA feature extraction using homomorphic filtering, and 94.4% for recognition.