Face Emotion Recognition for All Geographical Regions using Particle Swarm Optimization Algorithm and Feed Forward Network
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Abstract
One of best and easiest methods for emotion recognition is facial expressions. Facial expression gives important information about
emotion of a person. Face emotion recognition is one of main applications machine vision that widely attended in recent years. It can be used in
areas of security, entertainment and human machine interface (HMI). Emotion recognition usually uses of science image processing, speech
processing, gesture signal processing and physiological signal processing. In this paper particle swarm optimization algorithm using feed
forward neural network based on two dataset of images to face emotion recognition in two different geographical regions has been proposed. We
recommend use of eye and lip as biometric elements for face emotion recognition. Face emotion recognition process involves three stages preprocessing,
feature extraction and classification. One of biggest problems in classification emotion is overlap in range of values. In the other
words to increase accuracy in face emotion recognition we recommend use of feed forward neural network. This method used for two different
geographical regions (Indian and Japanese) and can be used for any geographical area with certain error. The obtained results show that success
rate and running speed for two different geographical areas are acceptable.
Keywords: Feature extraction, Projection profile, Particle swarm optimization algorithm and Feed forward network.
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