Optimization Eye and Lip Curves with Minimization Euclidean Distance and Use Learning Vector Quantization (LVQ) Network for Classification Face Emotion
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Abstract
One of simplest and most commonly methods for emotion recognition is facial expressions. Facial expression gives important
information about emotion of a person. Face emotion recognition is one of important issues that widely attended in recent years. It can be used in
areas of security, control, entertainment and machine vision. Nowadays for emotion recognition is used of science image processing, speech
signal processing, gesture signal processing and physiological signal processing. Our proposal uses of an objective function for minimization
sum of Euclidean distance from the given points to the eye and lip curve that PSO algorithm will be used to optimize objective function. We
recommend use of ellipse form as eye and lip in face emotion recognition. Face emotion recognition process like presented previous papers
involves three stages pre-processing, feature extraction and classification. One of biggest problems in classification emotions is overlap in range
of values. To increase success rate and running speed in face emotion recognition we used in this paper for another experience of neural
networks with name LVQ neural network. We also show our experiments, we want obtain better results than those previously reported.
Additionally our solutions have a low error in success rate.
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Keywords: Projection profile, Particle Swarm Optimization (PSO), Euclidean Distance Measurement LVQ Network.
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