• Fulltext


        Click here to view fulltext PDF

      Permanent link:

    • Keywords


      Facial expression recognition; convolutional neural networks (CNN); transfer learning; real-time detection.

    • Abstract


      This study aims to design a real-time application to detect several human beings’ universal emotional levels simultaneously. The intra-class and inter-class variations present in images make it one of the most challenging recognition problems. In this regard, a simple solution for facial expression recognition using a combination of convolutional neural network (CNN) with minimal parameters and transfer learning (TL) has been proposed here. The proposed CNN architecture named LiveEmoNet has been jointly trained with wild (FER-2013) and lab-controlled (CK+) datasets for real-time detection, contributing to versatile emotion detection. The observed experimental results demonstrate that the proposed method outperforms the other related researche concerning accuracy and time. The accuracy of 68.93%, 97.66%, and 96.67% has been achieved on FER-2013, JAFFE, and 7-classes of the CK+ dataset, respectively. Also, real-time detection takes 46.85 ms/frame with an intel i5 2.60 GHz CPU, which is significantly better than other works in the literature.

    • Author Affiliations



      1. Department of Computer Science and Engineering, National Institute of Technology Agartala, Agartala, Tripura, India
      2. Department of Computer Science and Engineering, Techno India University, Kolkata, West Bengal, India
    • Dates

  • Sadhana | News

    • Editorial Note on Continuous Article Publication

      Posted on July 25, 2019

      Click here for Editorial Note on CAP Mode

© 2022-2023 Indian Academy of Sciences, Bengaluru.