Abstract
Facial expressions play an important role in identifying the emotional state of an individual. Individuals can have different reactions to the same stimuli. Various emotions (anger, disgust, fear, joy, sadness, surprise or neutral) are detected while the user plays a game and their facial expressions are analyzed through a live web camera. Implementing the Haar Algorithm, the frames are cropped and the face alone is procured on which grey scaling and resizing process is carried out. Now the most necessary features of the face are extracted by a neural network model which will encode motion and facial expressions to predict emotion. In this paper, a linear model along with ResNeXt and PyramidNet models are analyzed side by side to identify the algorithm which gives the best accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mehendale, N.: Facial emotion recognition using Convolutional neural networks (FERC), Ninad’s Research Lab, Thane, India, 18 February 2020
Rescigno, M., Spezialetti, M., Rossi, S.: Personalized models for facial emotion recognition through transfer learning. Multimed. Tools Appl. 79(47–48), 35811–35828 (2020). https://doi.org/10.1007/s11042-020-09405-4
Verma, G., Verma, H.: Hybrid-deep learning model for emotion recognition using facial expressions. Rev. Socionetwork Strat. 14(2), 171–180 (2020). https://doi.org/10.1007/s12626-020-00061-6
Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.): ICAISC 2012. LNCS (LNAI), vol. 7267. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29347-4
Anjani Suputri Devi, D., Satyanarayana, Ch.: An efficient facial emotion recognition system using novel deep learning neural network-regression activation classifier. Multimed. Tools Appl. (2021)
Rani, P.I., Muneeswaran, K.: Emotion recognition based on facial components. Sadhana 43, 48 (2018)
González-Lozoya, S.M., de la Calleja, J., Pellegrin, L., et al.: Recognition of facial expressions based on CNN features. Multimed. Tools Appl. 79, 13987–14007 (2020)
Li, K., Jin, Y., Akram, M.W., Han, R., Chen, J.: Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy. Vis. Comput. 36(2), 391–404 (2019). https://doi.org/10.1007/s00371-019-01627-4
Caroppo, A., Leone, A., Siciliano, P.: Comparison between deep learning models and traditional machine learning approaches for facial expression recognition in ageing adults. J. Comput. Sci. Technol. 35(5), 1127–1146 (2020). https://doi.org/10.1007/s11390-020-9665-4
Lai, Z., Chen, R., Jia, J. et al. Real-time micro-expression recognition based on ResNet and atrous convolutions. J. Ambient Intell. Human. Comput. (2020)
Gruber, I., Hlavac, M., Zelezny, M., Karpov, A: Facing Face Recognition with Res-Net (2017)
Wang, Z., Zhou, X., Wang, W., Liang, C.: Emotion recognition using multimodal deep learning in multiple psychophysiological signals and video. Int. J. Mach. Learn. Cybern. 11(4), 923–934 (2020). https://doi.org/10.1007/s13042-019-01056-8
Cui, R., Plested, J., Liu, J.: Declarative Residual Network for Robust Facial Expression Recognition (2020)
Do, L.-N., Yang, H.-J., Nguyen, H.-D., Kim, S.-H., Lee, G.-S., Na, I.-S.: Deep neural network-based fusion model for emotion recognition using visual data. J. Supercomputing 77, 10773–10790 (2021)
Jie, S., Yongsheng, Q.: Multi-view facial expression recognition with multi-view facial expression LightWeight Network. Pattern Recognit. Image Anal. 30, 805–814 (2020)
Acknowledgement
We express our sincere thanks to PSG College of Technology for providing constant support and guidance throughout the project. We also acknowledge Google for providing a Colab platform for training and testing the Neural Network models for facial emotion recognition using deep learning.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kumar, B.V., Jayavarshini, R., Sakthivel, N., Karthiga, A., Narmadha, R., Saranya, M. (2022). Evaluation of Deep Architectures for Facial Emotion Recognition. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_47
Download citation
DOI: https://doi.org/10.1007/978-3-031-11346-8_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-11345-1
Online ISBN: 978-3-031-11346-8
eBook Packages: Computer ScienceComputer Science (R0)