One of best and easiest methods for emotion recognition is facial expressions. Facial expression gives important information aboutemotion of a person. Face emotion recognition is one of main applications machine vision that widely attended in recent years. It can be used inareas of security, entertainment and human machine interface (HMI). Emotion recognition usually uses of science image processing, speechprocessing, gesture signal processing and physiological signal processing. In this paper particle swarm optimization algorithm using feedforward neural network based on two dataset of images to face emotion recognition in two different geographical regions has been proposed. Werecommend 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 otherwords to increase accuracy in face emotion recognition we recommend use of feed forward neural network. This method used for two differentgeographical regions (Indian and Japanese) and can be used for any geographical area with certain error. The obtained results show that successrate 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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    Title :

    Face Emotion Recognition for All Geographical Regions using Particle Swarm Optimization Algorithm and Feed Forward Network


    Contributors:

    Publication date :

    2017-01-13


    Remarks:

    doi:10.26483/ijarcs.v3i1.1025
    International Journal of Advanced Research in Computer Science; Vol 3, No 1 (2012): January-February 2012; 22-25 ; 0976-5697 ; 10.26483/ijarcs.v3i1



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    006 / 629



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