A facial recognition application based on incremental supervised learning
Elena Roxana Buhuş, Lăcrimioara Grama, Catalina Serbu · 2017
Facial recognition applications present a great interest in the area of computer vision, with various methods and approaches that provide impressive performance. However, not all studies investigate the possibilities of using proper feature extraction methods with efficient classifiers, for applications that facial expression is not required for detection. In this sense, we propose another facial recognition application based on Local Binary Patterns or the fusion of Local Binary Patterns and Discrete Cosines Transform for feature extraction, with a classifier based on a Simplified Fuzzy Adaptive Resonance Theory Map neural network. Experiments results on two open source face databases (AT&T, Extended Yale B) show that the new approach achieves promising results.