FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-100466

Research Article |

Human Emotion Recognition using Deep Learning with Special Emphasis on Infant’s Face

Author(s): Parismita Sarma1, Takrim UL Islam Laskar2, Dankan Gowda V3, and Ramesha M4

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 4

Publisher : FOREX Publication

Published : 20 December 2022

e-ISSN : 2347-470X

Page(s) : 1176-1183

DOI: https://doi.org/10.37391/IJEER.100466

Abstract

This paper discusses a deep learning-based image processing method to recognize human emotion from their facial expression with special concentration on infant’s face between one to five years of age. The work has importance because most of the time it becomes necessary to understand need of a child from their facial expression and behavior. This work is still a challenge in the field of Human Facial Emotion Recognition due to confusing facial expression that sometimes found in some of the samples. We have tried to recognize any facial expression into one of the mostly understood human mood namely Angry, Disgust, Fear, Happy, Sad, Surprise and Neutral. For this purpose, we have trained an image classifier with Convolutional Neural Network with Kaggle's Fer2013 Dataset. After the completion of the project, we achieved good accuracy in most of the prominent emotions by testing with 20 random images for each emotion.

Keywords: Convolution Neural Network, Emotion detection, Deep Neural Network, Haar Cascade Frontal Face Detector.




Parismita Sarma*, Assistant Professor, Department of Information Technology, Gauhati University, Guwahati, Assam, India; Email: pari@gauhati.ac.in

Takrim UL Islam Laskar, Department of Information Technology, Gauhati University, Guwahati, Assam, India; Email: takrimulislam@gmail.com

Dankan Gowda V, Department of Electronics and Communication Engineering, BMS Institute of Technology and Management, Bangalore, Karnataka, India; Email: dankan.v@bmsit.in

Ramesha M, Assistant Professor, Department of Electronics and Communication Engineering, GITAM School of Technology, GITAM (DEEMED TO BE UNIVERSITY), Bengaluru, Karnataka, India; Email: rameshmalur037@gmail.com

    [1] Avinash Sharma, and A. Azhagu Jaisudhan Pazhani, A novel method of data compression using ROI for biomedical 2D images, Measurement: Sensors, Volume 24, 2022, 100439, ISSN 2665-9174.[Cross Ref]
    [2] Mayur Rahul, Namita Tiwari, Rati Shukla, Devvrat Tyagi and Vikash Yadav (2022), A New Hybrid Approach for Efficient Emotion Recognition using Deep Learning. IJEER 10(1), 18-22. DOI: 10.37391/IJEER.100103.[Cross Ref]
    [3] Shashank M Gowda and H N Suresh (2022), Facial Expression Analysis and Estimation Based on Facial Salient Points and Action Unit (AUs). IJEER 10(1), 7-17. DOI: 10.37391/IJEER.100102.[Cross Ref]
    [4] I. Goodfellow, D. Erhan, P.-L. Carrier, A. Courville, M. Mirza, B. Hamner, W. Cukierski, Y. Tang, D. Thaler, D.-H. Lee, Y. Zhou, C. Ramaiah, F. Feng, R. Li, X. Wang, D. Athanasakis, J. Shawe-Taylor, M. Milakov, J. Park, R. Ionescu, M. Popescu, C. Grozea, J. Bergstra, J. Xie, L. Romaszko, B. Xu, Z. Chuang, and Y. Bengio, “Challenges in representation learning: A report on three machine learning contests," Available at: http://arxiv.org/abs/1307.0414, Accessed on 11 March, 2021.[Cross Ref]
    [5] T. Nath, A. Mathis, A. C. Chen, A. Patel, M. Bethge and M. W. Mathis “Using DeepLabCut for 3D markerless pose estimation across species and behaviors”, Nature protocols, Vol. 14, No. 7, pp. 2152-2176, 2019.[Cross Ref]
    [6] H. Sadr, M. M. Pedram and M. Teshnehlab “A robust sentiment analysis method based on sequential combination of convolutional and recursive neural networks”, Neural Processing Letters, Vol. 50, No.3, pp.2745-2761, 2019.[Cross Ref]
    [7] M. Khan, S. Chakraborty, R. Astya & S. Khepra “Face Detection and Recognition Using OpenCV”, proceedings in 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), IEEE, pp. 116-119, 2019.[Cross Ref]
    [8] Z. Qin, H. Ye, G. Y. Li and B. H. F. Juang “Deep learning in physical layer communications”, IEEE Wireless Communications, Vol. 26, No. 2, pp. 93-99, 2019.[Cross Ref]
    [9] X. Jiang, Y. Pang, X. Li, J. Pan and Y. Xie “Deep neural networks with elastic rectified linear units for object recognition”, Neurocomputing, Vol. 275, pp. 1132-1139, 2018.[Cross Ref]
    [10] Agarap, A. F. “Deep Learning using Rectified Linear Units (ReLU”, Cornell University, arXiv preprint arXiv: 1803.08375, 2018.[Cross Ref]
    [11] N. Zeng, H. Zhang, B. Song, W. Liu, Y. Li, and A. M. Dobaie, “Facial expression recognition via learning deep sparse autoencoders", Neurocomputing, Vol. 273, pp. 643-649, 2018.[Cross Ref]
    [12] A. Savoiu and J. Wong, “Recognizing facial expressions using deep learning," Stanford University, 2017.[Cross Ref]
    [13] S. K. A. Kamarol, M. H. Jaward, J. Parkkinen, and R. Parthiban, “Spatiotemporal feature extraction for facial expression recognition", IET Image Processing, Vol. 10, No. 7, pp. 534-541, 2016.[Cross Ref]
    [14] M. Rabiei and A. Gasparetto, “System and method for recognizing human emotion state based on analysis of speech and facial feature extraction; applications to human-robot interaction," in 2016 4th International Conference on Robotics and Mechatronics (ICROM). IEEE, pp. 266-271, 2016.[Cross Ref]
    [15] Pascu, A., Intelligence, B. A., & King, R. “Facial Expression Recognition System”, University of Manchester, 2015.[Cross Ref]
    [16] D. Lapakko “Communication is 93% nonverbal: An urban legend proliferates”, Communication and Theater Association of Minnesota Journal, Vol. 34, No. 1, 2007.[Cross Ref]

Parismita Sarma, Takrim UL Islam Laskar, Dankan Gowda V and Ramesha M (2022), Human Emotion Recognition using Deep Learning with Special Emphasis on Infant’s Face. IJEER 10(4), 1176-1183. DOI: 10.37391/IJEER.100466.