Abstract

Facial emotion detection, a pivotal component of Artificial Intelligence (AI) and Computer Vision (CV), aims to recognize and identify human emotions from facial expressions. This paper presents an approach leveraging the Deep Learning (DL) models that includes Convolution Neural Network (CNN), Dual-Temporal Scale Convolutional Neural Networks (DTSCNN), Recurrent Neural Networks (RNN), and Residual Networks (ResNet-50) to achieve real-time and accurate emotion recognition. The primary objectives encompass real-time emotion recognition, high accuracy, low latency, and robustness to varied conditions. This paper performs experiments on benchmark datasets for evaluating each model considering accuracy, processing speed and facial orientations. This paper highlights the study of comparison between these models. The outcomes indicate that the CNN approach outperforms other methods, yielding superior accuracy and robustness. This research contributes in the advancement of facial emotion detection, with implications for applications in human-computer interaction, psychology, marketing, and healthcare. The CNN ensemble model represents a significant advancement in facial emotion detection, offering a comprehensive solution with broad implications across diverse domains. Its effectiveness highlights the significance of continuous exploration and refinement of deep-learning techniques to address complex tasks in CV and AI effectively.

References

• Pandey, Amit & Gupta, Aman & Shyam, Radhey. (2022). FACIAL EMOTION DETECTION AND RECOGNITION. 7. 176-179. 10.33564/IJEAST. 2022.v07i01.027.
• Mehta D, Siddiqui MFH, Javaid AY. Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality. Sensors (Basel). 2018 Feb 1;18(2):416. doi: 10.3390/s18020416. PMID: 29389845; PMCID: PMC5856132.Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
• C. Dalvi, M. Rathod, S. Patil, S. Gite and K. Kotecha, "A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions," in IEEE Access, vol. 9, pp. 165806-165840, 2021, doi: 10.1109/ACCESS.2021.3131733.
• Huang, ZY., Chiang, CC., Chen, JH. et al. A study on computer vision for facial emotion recognition. Sci Rep 13, 8425 (2023). https://doi.org/10.1038/s41598-023-35446-4
• Badr, Amr & Khalil, Mahmoud & Abbas, Hazem. (2018). Emotion Recognition by Facial Features using Recurrent Neural-Networks.417-422.0.1109/ICCES.2018.8639182. Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.
• M. Sanchez-Ruiz, J. Flores-Monroy, M. Nakano-Miyatake, E. Escamilla-Hernandez and H. Perez-Meana, "Face Expression Recognition using Recurrent Neural Networks," 2023 46th International Conference on Telecommunications and Signal Processing (TSP), Prague, Czech Republic, 2023, pp. 148-153, doi: 10.1109/TSP59544.2023.10197740.
• Bin Li, Dimas Lima, Facial expression recognition via ResNet-50, International Journal of Cognitive Computing in Engineering, Volume 2, 2021, Pages 57-64,
• Dhankhar Poonam. “ResNet-50 and VGG-16 for recognizing Facial Emotions.” (2019).
• I. Agrawal, A. Kumar, D. Swathi, V. Yashwanthi and R. Hegde, "Emotion Recognition from Facial Expression using CNN," 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), Bangalore, India, 2021, pp. 01-06, doi: 10.1109/R10-HTC53172.2021.9641578.
• Poonam Dhankar, ResNet-50 and VGG-16 for recognizing Facial emotions, International Journal of Innovations in Engineering and Technology, Volume 13 Issue 4, Pages 126-130