A New Neuro-Fuzzy Classifier with Application to On-Line Face Detection and Recognition

Abstract

In this paper, we propose a neuro-fuzzy classifier (NEFCAR) that utilizes positive and negative rules with different rule importances to create the decision boundaries between different classes. The locally unsupervised and globally supervised training technique is adopted. The decision-based and approximation-based strategies are combined to provide a suitable amount of training for each training pattern. The reinforced and anti-reinforced learning rules are given with different weighting so that the training can be efficient and can reach convergence quickly. Moreover, NEFCAR can easily provide the confidence measure of each classification decision. Therefore, the rejection algorithm can be implemented in a straightforward manner. Noise tolerant training is conducted to improve the generalization performance and the confidence measure is adopted to avoid overtraining. The proposed classifier is applied to two applications. The first one is the Fisher iris data classification, and the second one is an on-line face detection and recognition application. Good classification results are obtained in both applications. In the on-line face detection and recognition system, two NEFCAR's are utilized: a two-class and a multi-class NEFCAR's are adopted to detect the face and recognize the face, respectively. The color of skin and the motion information are taken into consideration heuristically to improve the effectiveness of the face location algorithm.