Indian Journal of Science and Technology
Year: 2020, Volume: 13, Issue: 31, Pages: 3222-3229
J Sujanaa1*, S Palanivel2
1Research Scholar, Department of Computer Science and Engineering, Annamalai University, Annamalainagar, 608002, Tamil Nadu, India. Tel.: +91-7010181616
2Professor, Department of Computer Science and Engineering, Annamalai University,Annamalainagar, 608002, Tamil Nadu, India
Email: [email protected]
Received Date:11 July 2020, Accepted Date:15 August 2020, Published Date:31 August 2020
Background/Objectives: The deep learning approaches have paved their way to construct various artificial intelligence products and the proposed system uses a convolutional neural network for detecting real-time emotions of mankind. The objective of the study is to develop a real-time application for emotion recognition using convolutional neural network and transfer learning methods. Methods/Statistical analysis: The proposed system considers happy, normal and surprised categories of emotions. The system consists of four major steps: dataset collection, training, validation, and real-time testing. The dataset is comprised of face images containing emotions such as happy,normal and surprised in the form of video frames. The face and mouth regions are detected using the Haar-Based Cascade classifier at 20 frames per second. Findings: The convolutional neural network (CNN) is trained using mouth images and the pre-trained models VGG16 and VGG19 are trained with face images. The trained model is used to detect the emotions in the live webcam video. The experimental results show that the CNN model trained using mouth images gives an accuracy of 85.71% and the pre-trained models trained with face images using transfer learning method achieves an accuracy of 77.78%. The proposed system using CNN outperforms the pre-trained models for recognizing the emotions in real-time video. Novelty/Applications:The proposed system is entirely based on the mouth region video frames and the real-time emotion recognition system is developed. This work can detect the three emotions in an unconstrained laboratory environment.
Keywords: Convolutional neural network; mouth detection; pre-trained models; real-time emotion recognition; transfer learning
© 2020 Sujanaa & Palanivel.This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee).
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