Time efficient real time facial expression recognition with CNN and transfer learning
TANUSREE PODDER DIPTENDU BHATTACHARYA ABHISHEK MAJUMDAR
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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.
TANUSREE PODDER1 DIPTENDU BHATTACHARYA1 ABHISHEK MAJUMDAR2
Volume 48, 2023
Continuous Article Publishing mode
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