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
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
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 :
- D. Phutela, “The importance of non-verbal communication,” IUP J. Soft Skills, vol. 9, no. 4, p. 43, 2015.
- 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.
- 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)
- 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.
- 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:
- 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”
- 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.
- T. Gremsl and E. Ho¨dl, “Emotional AI: Legal and ethical challenges,” Inf. Polity, vol. 27, no. 2, pp. 1–12, Apr. 2022.
- 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.
- 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.
- 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.
- 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).
- Facial Expression Recognition 2013 (FER2013) Dataset Available: https://www.kaggle.com/datasets/msambare/fer2013
- 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).
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.