A 'personalized' facial expression recognition with fuzzy similarity measure and novel feature selection method
Dae‐Jin Kim, Zeungnam Bien · 2005
We discuss about a design process of 'personalized' classifier with soft computing techniques. Based on a human's way of thinking, a construction methodology for personalized classifier is mentioned. Here, two fuzzy similarity measures and ensemble of classifiers are effectively used. A feature selection method for the fuzzy neural networks plays also a key role for construction of personalized classifier. As one of the possible applications, facial expression recognition problem is discussed. The numerical result shows that the proposed method can enhance the classification rate for 22 persons from 81.0% to 90.4%.