Authors : Anandhu T. G.; Areena Aji; Jithin K. A.; Sukanyathara J; Rotney Roy Meckamalil

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/set2prhr

Scribd : https://tinyurl.com/yc773x2e

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY1322

Abstract : Individuals with visual impairment often face chal- lenges in social interactions, specifically at recognizing emotional cues. The proposed framework tackles this issue head-on by de- vising a Facial Emotion Recognition(FER) system, by employing an advanced Transfer Learning approach within Convolutional Neural Networks (CNNs). By leveraging the dataset FER-2013 [13], the proposed system aims to transcend the limitationsof traditional emotion recognition methods. Transfer learningallows the model to benefit from pre-trained knowledge on vast datasets, making it more efficient and effective in capturing complex facial features associated with different emotions. This approach is designed to offer better accuracy and generalization capabilities than other conventional methods. During training, the system will be designed to comprehensively capture the intricacies of facial expressions, enabling it to not only identify individuals but also interpret subtle changes in their emotional states throughout conversations. An innovative audio output system will be integrated into the FER system to provide a smoothand accessible experience for visually impaired users, allowing for a better understanding of social dynamics. By emphasizing transfer learning, this framework is designed to be efficient and robust, potentially revolutionizing emotional understanding for visually impaired individuals and setting a new standard in the field by showcasing the superior performance achievable throughadvanced machine learning techniques. Ultimately, this research aims to bridge the social gap for the visually impaired by fosteringinclusivity, independence, and safety in their daily life.

Keywords : Visually Impaired, Facial Emotion Recognition, Transfer Learning, Convolutional Neural Networks, Computer Vi- Sion, Facial Recognition.

References :

1. D. Phutela, “The importance of non-verbal communication,” IUP J. Soft Skills, vol. 9, no. 4, p. 43, 2015.
2. Shehada, D., Turky, A., Khan, W., Khan, B., Hussain, A.(2023). A Lightweight Facial Emotion Recognition System Using Partial Transfer Learning for Visually Impaired People. IEEE Access, 11, 36961-36969.
3. Liu, Xia, Zhijing Xu, and Kan Huang. ”Multimodal Emotion Recogni- tion Based on Cascaded Multichannel and Hierarchical Fusion.” Com- putational Intelligence and Neuroscience 2023 (2023)
4. Shahzad, H. M., Bhatti, S. M., Jaffar, A., Akram, S., Alhajlah, M., Mahmood, A. (2023). Hybrid Facial Emotion Recognition Using CNN- Based Features. Applied Sciences, 13(9), 5572.
5. Arsirii O.O., Petrosiuk D.V. “An adaptive convolutional neural network model for human facial expression recognition”. Herald of Advanced Information Technology. 2023; Vol. 6 No. 2. 128–138. DOI:
6. R. Kishore Kanna, Bhawani Sankar Panigrahi, Susanta Kumar Sahoo, Anugu Rohith Reddy ,Yugandhar Manchala, Nirmal Keshari Swain (2024). ”CNN Based Face Emotion Recognition System for Healthcare Application”
7. S. Du, Y. Tao, and A. M. Martinez, “Compound facial expressions of emotion,” Proc. Nat. Acad. Sci. USA, vol. 111, no. 15, pp. E1454–E1462, Apr. 2014.
8. T.  Gremsl and  E.  Ho¨dl,  “Emotional AI:  Legal  and  ethical challenges,” Inf. Polity, vol. 27, no. 2, pp. 1–12, Apr. 2022.
9. Y. Huang, C. Dong, X. Luo, and Q. Dai, “Facial expression recognition algorithm based on improved VGG16 network,” in Proc. 6th Int. Symp. Comput. Inf. Process. Technol. (ISCIPT), Jun. 2021, pp. 480–485.
10. M. A. Mazen, A. A. Nashat, and R. A. A. A. A. Seoud, “Real time face expression recognition along with balanced FER2013 dataset using CycleGAN,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 6, pp. 1–12, 2021.
11. C. Porusniuc, F. Leon, R. Timofte, and C. Miron, “Convolutional neural networks architectures for facial expression recognition,” in Proc. E-Health Bioeng. Conf. (EHB), Nov. 2019, pp. 1–6.
12. Pushpalatha, M.N., Meherishi, H., Vaishnav, A. et al. Facial emotion recognition and encoding application for the visually impaired. Neural Comput Applic 35, 749–755 (2023).
13. Facial Expression Recognition 2013 (FER2013) Dataset Available: https://www.kaggle.com/datasets/msambare/fer2013
14. Vaidya, K.S., Patil, P.M. Alagirisamy, M. Hybrid CNN-SVM Classifier for Human Emotion Recognition Using ROI Extraction and Feature Fusion. Wireless Pers Commun 132, 1099–1135 (2023).