Facial expression recognition has been researched much in recent years because of their applications in intelligent communication systems. Many methods have been developed based on extracting Local Binary Pattern (LBP) features associating different classifying techniques in order to get more and more better effects of facial expression recognition. In this work, we propose a novel method for recognizing facial expressions based on Local Binary Pattern features and Support Vector Machine with two effective improvements. First is the preprocessing step and second is the method of dividing face images into nonoverlap square regions for extracting LBP features. The method was experimented on three typical kinds of database: small (213 images), medium (2040 images) and large (5130 images). Experimental results show the effectiveness of our method for obtaining remarkably better recognition rate in comparison with other methods.
A fuzzy associative memory is a fuzzy logic tool for pattern recognition or control problems. Fuzzy inference systems based on fuzzy associative memory have a wide range of practical applications. These systems need to be determined how to form and how many membership functions for each input variable by analyzing its histogram. This paper proposes an algorithm for optimizing their membership functions by a threshold set and a method that finds out optimal membership functions whereby classification effects of the fuzzy inference systems can be improved. The algorithm is associated with a measure of useful degree of input membership functions to increase accurate classification rates of the system. The paper also based on the experiment to show criteria for collecting training data to improve the effect of recognition or classification of the fuzzy inference systems. To confirm the effectiveness, the proposed algorithm is applied to a pattern recognition problem with the iris data through a fuzzy inference system based on fuzzy associative memory.
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