REAL-TIME FACIAL EXPRESSION RECOGNITION USING CNNS

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Facial expression recognition (FER) plays a vital role in human-computer interaction, enabling machines to understand and respond to human emotions. In this work, we present a real-time facial expression recognition system leveraging Convolutional Neural Networks (CNNs). The proposed approach focuses on accurately detecting and classifying facial expressions, such as happiness, sadness, anger, surprise, fear, and neutrality, in dynamic environments. Our system integrates advanced CNN architectures optimized for processing real-time video streams, ensuring a balance between accuracy and computational efficiency. The framework involves preprocessing stages such as face detection, alignment, and normalization, followed by feature extraction through deep CNN layers. The extracted features are then classified into distinct emotion categories using a fully connected network and softmax activation. To achieve real-time performance, we employ model optimization techniques, including quantization, pruning, and hardware acceleration with Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs). The model is trained on publicly available FER datasets, such as FER2013 and CK+, augmented with domain-specific enhancements to improve generalization to real-world scenarios. Experimental results demonstrate that the proposed system achieves high accuracy and robustness across diverse lighting conditions, occlusions, and variations in facial orientations. Additionally, the real-time implementation is benchmarked to run efficiently on resource-constrained devices, making it suitable for applications such as surveillance, assistive technologies, virtual reality, and interactive gaming. This study underscores the potential of CNNs in enabling real-time facial expression recognition, offering significant advancements for emotion-aware systems and fostering seamless interaction between humans and intelligent machines.

References

[1] Liu, K.-C., & Chen, C.-H. (2019). Real-Time Facial Expression Recognition Based on CNN. IEEE Access, 7, 122980-122990.

[2] Khan, M. A., & Sohail, A. (2019). Real Time Emotion Recognition from Facial Expressions Using CNN Architecture. IEEE Access, 7, 123233-123243.

[3] Zhang, Y., & Zhang, C. (2023). Real-Time Facial Expression Recognition via Quaternion Gabor CNN. SSRN Electronic Journal.

[4] Naveen, N. C., Smaran, K. S., & Shamitha, A. S. (2024). Real Time Facial Emotion Recognition using Deep Learning Models. International Journal of Computer Applications, 186(29), 41-45.

[5] Ali, I., & Ghaffar, F. (2024). Robust CNN for Facial Emotion Recognition and Real-Time GUI. AIMS Electronics and Electrical Engineering, 8(2), 227-246.

[6] Oguine, O. C., Oguine, K. J., Bisallah, H. I., & Ofuani, D. (2022). Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction. arXiv preprint arXiv:2206.09509.

[7] Lee, J. R. H., & Wong, A. (2020). TimeConvNets: A Deep Time Windowed Convolution Neural Network Design for Real-Time Video Facial Expression Recognition. arXiv preprint arXiv:2003.01791.

[8] Koujan, M. R., Alharbawee, L., Giannakakis, G., Pugeault, N., & Roussos, A. (2020). Real-Time Facial Expression Recognition 'In The Wild' by Disentangling 3D Expression from Identity. arXiv preprint arXiv:2005.05509.

[9] Tommola, J., Ghazi, P., Adhikari, B., & Huttunen, H. (2018). Real Time System for Facial Analysis. arXiv preprint arXiv:1809.05474.

[10] Shan, C., Gong, S., & McOwan, P. W. (2009). Facial Expression Recognition Based on Local Binary Patterns: A Comprehensive Study. Image and Vision Computing, 27(6), 803-816.

[11] Rajkumar, V., and V. Maniraj. "HYBRID TRAFFIC ALLOCATION USING APPLICATION-AWARE ALLOCATION OF RESOURCES IN CELLULAR NETWORKS." Shodhsamhita (ISSN: 2277-7067) 12.8 (2021).

[12] Ambika, G., and P. Srivaramangai. "REVIEW ON SECURITY IN THE INTERNET OF THINGS." International Journal of Advanced Research in Computer Science 9.1 (2018).

[13] Rosy, C. Premila, and R. Ponnusamy. "Evaluating and forecasting room demand in tourist spot using Holt-Winters method." International Journal of Computer Applications 975 (2017): 8887.

[14] D.Ragupathi, N.Jayaveeran, “The Design & Implementation of Transportation Procedure using Migration Techiques,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.273-278, 2017.

[15] Rajkumar, V., and V. Maniraj. "RL-ROUTING: A DEEP REINFORCEMENT LEARNING SDN ROUTING ALGORITHM." JOURNAL OF EDUCATION: RABINDRABHARATI UNIVERSITY (ISSN: 0972-7175) 24.12 (2021).

[16] Ambika, G., and P. Srivaramangai. "A study on data security in Internet of Things." Int. J. Comput. Trends Technol. 5.2 (2017): 464-469.

[17] C.Senthil Selvi, Dr. N. Vetrivelan, “ Medical Search Engine Based On Enhanced Best First Search International Journal Of Research And Analytical Reviews (IJRAR.ORG) 2019, Volume 6, Issue 2, Page No: 248-250.

[18] Rajkumar, V., and V. Maniraj. "Software-Defined Networking's Study with Impact on Network Security." Design Engineering (ISSN: 0011-9342) 8 (2021).

[19] Ambika, G., and D. P. Srivaramangai. "A study on security in the Internet of Things." Int. J. Sci. Res. Comput. Sci. Eng. Inform. Technol 5.2 (2017): 12-21.

[20] K.U. Malar, D. Ragupathi, G.M. Prabhu, “The Hadoop Dispersed File system: Balancing Movability And Performance”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.166-177, 2014.

[21] D. Ragupathi , S.Sivaranjani, “Performance Enhanced Live Migration of Virtual Machines in the Cloud,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.94-99, 2015.

[22] Rosy, C. P. R. O. M., and R. Ponnusamy. "A Study on Hotel Reservation Trends of Mobile App Via Smartphone." IOSR Journal of Computer Engineering (IOSR-JCE) 19.4 (2017): 01-08.

[23] Rajkumar, V., and V. Maniraj. "HCCLBA: Hop-By-Hop Consumption Conscious Load Balancing Architecture Using Programmable Data Planes." Webology (ISSN: 1735-188X) 18.2 (2021).

[24] Ambika, G., and P. Srivaramangai. "Encrypted Query Data Processing in Internet Of Things (IoTs): CryptDB and Trusted DB." (2018).

[25] Rajkumar, V., and V. Maniraj. "Dependency Aware Caching (Dac) For Software Defined Networks." Webology (ISSN: 1735-188X) 18.5 (2021).

[26] C.Senthil Selvi, Dr. N. Vetrivelan, “ An Efficient Information Retrieval In Mesh (Medical Subject Headings) Using Fuzzy”, Journal of Theoretical and Applied Information Technology 2019. ISSN: 1992-8645, Vol.97. No 9, Page No: 2561-2571.

[27] Rosy, C. Premila, and R. Ponnusamy. "Intelligent System to Support Judgmental Business Forecasting: The Case of Unconstraint Hotel  RoomDemand in Hotel Advisory System." International Journal of Science and Research (IJSR) 4.1 (2015).

[28] C.Senthil Selvi, Dr. N. Vetrivelan, “ Medical Search Engine Based On Enhanced Best First Search International Journal Of Research And Analytical Reviews (IJRAR.ORG) 2019, Volume 6, Issue 2, Page No: 248-250.

[29] D. Ragupathi and N. Jayaveeran, "Significant role of migration in virtual environment," 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), Pudukkottai, India, 2016, pp. 1-6, doi: 10.1109/ICETETS.2016.7603122.

[30] M. Dhivya, D. Ragupathi, V.R. Kumar, “Hadoop Mapreduce Outline in Big Figures Analytics,” International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.100-104, 2014.

Keywords

Facial Expression Recognition (FER), Convolutional Neural Networks (CNNs), Real-Time Emotion Detection, Human-Computer Interaction, Deep Learning-Based FER.