Multimedia ResearchISSN:2582-547X

Optimization Assisted Convolutional Neural Network for Facial Emotion Recognition

Abstract

Facial Expression Recognition (FER) is an important type of visual information that can be used to understand a human‟s emotional situation. FER has attained a significant interest in human-computer interaction, autopilot, medical healing as well as various face expression dependent areas, and it is enormously used in most research areas. Hence, this paper intends to develop an intelligent facial emotion recognition model by following two major processes namely (a) Feature extraction and (b) Classification. Initially, the input image is subjected to extract Local Binary Pattern (LBP) based features. Further, the extracted features are classified using a Convolutional neural network (CNN). Moreover, the weights of CNN are optimally tuned by the Improvised Steering angle and Gear-based ROA (ISG-ROA) algorithm. Finally, the superiority of the ISG-ROA method is compared over existing methods and its improvement is proved effective.

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DOI : https://doi.org/10.46253/j.mr.v3i2.a5

Author information

Affiliations

Sanjay Ghodawat Group of Institutes, Hatkanangale, Maharashtra,India

Publisher Information

  • Received

    14 December

  • Revised

    14 February

  • Accepted

    9 March

Keywords
Facial Emotion; Expressions; Rider Optimization; Steering angle; Convolutional Neural Network; Local Binary Pattern.

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