The most natural, influential and powerful way to communicate or convey a message is face expressions. In the field of computer engineering, facial expression recognition system, is helpful in areas like healthcare system, computer graphics, biometric devices, mobile phones, etc. Technologies such as virtual reality (VR) and augmented reality (AR) make use of facial expression recognition to implement a natural, friendly communication with humans. In this paper an approach for Facial Expression Recognition using Ensemble Learning Technique has been proposed. Ensemble methods use various learning algorithms to obtain good predictive performance that could be obtained from any of the basic learning algorithms alone. In the proposed method, initially the features are extracted from static images using color histograms. This process is done for all images gathered in the training dataset. The ensemble technique is then applied on the featured dataset in order to categorize a given image into one of the six emotions, happy, sad, fear, angry, disgust, and surprise. A satisfactory result has been obtained using static image dataset taken from kaggle and uci machine learning repository
Multiobjective sparse ensemble learning by means of evolutionary algorithms
