Investigate the Performance of Fuzzy Artmap Classifier for Face Recognition System

Face recognition has become one of the most active research areas of computer vision. In this paper we present a face recognition system (FRS) based on eigenfaces as feature extractor and fuzzy artmap (FAM) neural network as classifier. The motivation of using FAM as a classifier is because of its unique solution to the stability-plasticity dilemma, where it has the ability to preserve previously learned knowledge and potential to adapt new patterns indefinitely. FAM is also used to overcome the problem of long training duration and incremental learning without forgetting the previous learnt data. The FRS applies homomorphic filtering for preprocessing. The paper explains the methodology used and discusses on the experiments conducted to investigate the performance of the FRS using fuzzy artmap. From the experiments, the proposed FRS obtained a recognition rate of 97.5% using local dataset and 98% using Olivetti Research Lab (ORL) dataset.