Bee royalty offspring algorithm for improvement of facial expressions classification model
International Journal of Bio-Inspired Computation
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Facial expression is the most natural and instinctive means for human beings to communicate with each other Automatic analysis of human facial expression remains a very challenging area of research in computer vision and machine learning. In this paper, a novel human emotion detection is proposed based on well-known local binary pattern (LBP) and a newly developed feature matrix. Facial feature is extracted using LBP. A unique feature matrix is then combined to apply to Adaptive Neuro Fuzzy Inference System to generate five facial expression models, namely, happy, sad, angry, disgust and surprise. A number of experiments are carried out on facial expression determination with different LBP techniques by varying the number of points and radius of neighbourhood to JAFFE face database. The proposed system achieves very high accuracy with LBP(16,2) which has outperformed most of the existing methods.