Biometrics Solutions for Authentication Disability People: A survey
Abstract
Biometric authentication systems have become an essential component of modern security applications due to their ability to provide reliable and user-friendly identity verification. Traditional authentication methods such as passwords and PINs are often difficult for people with disabilities to use effectively. As a result, researchers have explored biometric technologies that provide accessible, secure, and convenient authentication solutions for individuals with visual, hearing, speech, physical, and hand disabilities. This survey paper reviews various biometric authentication methods designed for people with disabilities, including iris recognition, sclera recognition, finger vein recognition, hand vein recognition, electrocardiogram (ECG), palmprint recognition, facial thermogram, retina scanning, and voice recognition. The paper discusses the strengths, limitations, security challenges, and usability considerations of these methods. Furthermore, the survey highlights current research gaps and future directions for developing inclusive and multimodal biometric systems that can improve accessibility and security for disabled users.
References
[1] K. Holloway, R. Al Masri, and A. Abu Yahia, “Digital identity, biometrics and inclusion in humanitarian responses to refugee crises,” HPG Working Paper, Overseas Development Institute (ODI), London, U.K., Oct. 2021.
[2] B. M. Alsellami and P. D. Deshmukh, “The recent trends in biometric traits authentication based on Internet of Things (IoT),” in Proc. 2021 Int. Conf. Artif. Intell. Smart Syst. (ICAIS), 2021, doi: 10.1109/ICAIS50930.2021.9396007.
[3] R. Ryu, S. Yeom, S.-H. Kim, and D. Herbert, “Continuous multimodal biometric authentication schemes: A systematic review,” IEEE Access, vol. 9, pp. 34541–34557, 2021, doi: 10.1109/ACCESS.2021.3061589.
[4] M. Gomez-Barrero, P. Drozdowski, C. Rathgeb, J. Patino, M. Todisco, A. Nautsch, N. Damer, J. Priesnitz, N. Evans, and C. Busch, “Biometrics in the era of COVID-19: Challenges and opportunities,” IEEE Trans. Technol. Soc., vol. 3, no. 4, pp. 307–322, 2022, doi: 10.1109/TTS.2022.3203571.
[5] P. Drozdowski, F. Stockhardt, C. Rathgeb, D. Osorio-Roig, and C. Busch, “Feature fusion methods for indexing and retrieval of biometric data: Application to face recognition with privacy protection,” IEEE Access, vol. 9, pp. 139361–139378, 2021, doi: 10.1109/ACCESS.2021.3118830.
[6] S. A. Abdulrahman and B. Alhayani, “A comprehensive survey on the biometric systems based on physiological and behavioural characteristics,” Materials Today: Proceedings, vol. 80, pp. 2642–2646, 2023, doi: 10.1016/j.matpr.2021.07.005.
[7] World Health Organization (WHO), 2026. [Online]. Available: http://www.who.int/mediacentre/factsheets/fs282/en/
[8] N. K. Bhat, Y. B. Gajara, H. U. Rajgor, and S. P. Ambadekar, “ABHIGYAAN: Source of knowledge for visually impaired using vibrations,” Advances in Computational Sciences and Technology, vol. 11, no. 1, pp. 1–4, 2018.
[9] Z. Rui and Z. Yan, “A survey on biometric authentication: Toward secure and privacy-preserving identification,” IEEE Access, vol. 7, pp. 5994–6009, 2019, doi: 10.1109/ACCESS.2018.2889996.
[10] G. Lavanya, R. Rithika, P. S. Sanjana, and G. K. ShilpaTharani, “EYE-TM: An automatic teller machine for the visually impaired,” in Proc. Int. Conf. Comput., Commun., Electr. Biomed. Syst., 2022, pp. 503–507.
[11] J. S. A. Jeba Sheela, M. S. Meenakshi, N. S. Narmatha, and P. A. Pavithra, “Survey paper on authentication and voice assistance techniques to assist visually challenged people,” International Journal of Real-Time Applications and Computing Systems (IJORTACS).
[12] I. A. C. Iluno, O. C. Okeke, and I. Mgbeafuluike, “Development of a deep learning technology to improve human computer interaction for visually impaired,” International Journal of Real-Time Applications and Computing Systems (IJORTACS), vol. 3, no. 47, pp. 613–627, Oct. 2024. [Online]. Available: http://www.ijortacs.com
[13] M. M. R. Akanda, A. Lacy, and N. Saxena, “SoK: Inaccessible & insecure: An exposition of authentication challenges faced by blind and visually impaired users in state-of-the-art academic proposals,” in Proc. 34th USENIX Security Symp., Seattle, WA, USA, Aug. 2025.
[14] C. Toussaint, B. Chateau, P.-G. Gourio-Jewell, E. Bonnefoy, and N. Louveton, “Inclusive by design: Developing barrier-free authentication for blind and low vision users through the ALIAS project,” in Proc. ECCE 2025, Tallinn, Estonia, 2025, doi: 10.1145/3746175.3746210.
[15] A. B. Prastyo and A. Setyanto, “Analysis of FaceNet and VGG16 for blind face recognition with MTCNN and HaarCascade detection methods,” G-Tech: Jurnal Teknologi Terapan, vol. 9, no. 2, pp. 570–577, 2025.
[16] R. J. Oommen and T. Sharma, “Essential, yet overlooked: Identity verification barriers for blind and low vision people in government services,” arXiv preprint arXiv:2604.28166, Apr. 30, 2026, doi: 10.48550/arXiv.2604.28166.
[17] A. M. Qamar, A. Murad, M. A. Rahman, F. Ur Rehman, A. Ahmad, B. Sadiq, and S. M. Basalamah, “A multi-sensory gesture-based login environment,” in Proc. 23rd ACM Int. Conf. Multimedia (MM ’15), Brisbane, Australia, 2015, pp. 763–764, doi: 10.1145/2733373.2807987.
[18] C. R. Fenner and C. Noteboom, “How wearable technology will replace verbal authentication or passwords for universal secure authentication for healthcare,” in Proc. MWAIS, 2018.
[19] A. Adegbengle, U. Nzenwata, O. Rotimi, A. Oreoluwa, and A. James, “Ethics in biometrics authentication,” GSJ: Global Scientific Journal, vol. 8, no. 9, Sep. 2020.
[20] M. P. Paing, A. Juhong, and C. Pintavirooj, “Design and development of an assistive system based on eye tracking,” Feb. 2022.
[21] R. Aboalela, S. Sebaa, A. Almohamadi, R. Almohammadi, and G. Alzebali, “Hold My Hand application for disabilities,” International Journal on Cybernetics & Informatics (IJCI), vol. 11, no. 4, Aug. 2022.
[22] M. Kamil and S. Khalid, “Real-time Siamese neural network based algorithm for user recognition by their eye blinking,” 2024.
[23] K. Pushpavalli, D. Sathis Pandy, S. Tharun, and K. Sundaramoorthy, “Secure e-voting system with biometric authentication,” in Proc. Int. Conf. Comput. Sci. Commun. Eng. (ICCSCE 2025), Advances in Computer Science Research, vol. 124, 2025, doi: 10.2991/978-94-6463-858-5_275.
[24] M. L. Ali, G. Goldberg, J. Guzman, W. Huang, A. Manuele, and H. Patel, “Keystroke biometric authentication on short numeric input on mobile devices,” 2016, pp. 1–4.
[25] E. J. A. P. C. Chathumali, J. M. U. B. Jayasekera, D. C. Pinnawala, S. M. T. K. Samaraweera, and A. Gamage, “SPEECUR: Intelligent PC controller for hand disabled people using NLP and image processing,” Sri Lanka Institute of Information Technology, Sri Lanka, 2016.
[26] A. Alhayajneh, A. N. Baccarini, G. M. Weiss, T. Hayajneh, and A. Farajidavar, “Biometric authentication and verification for medical cyber physical systems,” Electronics, vol. 7, no. 12, Art. no. 436, 2018, doi: 10.3390/electronics7120436.
[27] B. Lewis, “Designing accessible computing device authentication for people with upper extremity impairment,” Ph.D. dissertation, Dept. Computer Science and Statistics, University of Rhode Island, Kingston, RI, USA, 2023, doi: 10.23860/diss-lewis-brittany-2023.
[28] M. C. Banville, “Am I who I say I am? The illusion of choice: Biometric identification in healthcare,” Ph.D. dissertation, Department of English, East Carolina University, Greenville, NC, USA, 2023.
[29] A. Samraj, N. Mehrdel, and S. Sayeed, “Sign language communication and authentication using sensor fusion of hand glove and photometric signals,” in Proc. 8th Int. Conf. Inf. Technol. (ICIT), 2017, pp. 1–7.
[30] U. Patel and A. G. Ambekar, “Recognition & translation of hand gesture for Indian sign language based on KNN & PNN classifier,” Advances in Computational Sciences and Technology, vol. 11, no. 1, pp. 19–24, 2018.
[31] A. Guo, E. Kamar, J. W. Vaughan, H. Wallach, and M. R. Morris, “Toward fairness in AI for people with disabilities: A research roadmap,” in Proc. ACM ASSETS 2019 Workshop on AI Fairness for People with Disabilities, USA, 2019, pp. 1–4.
[32] S. Nafisah and N. Effendy, “Voice biometric system: The identification of the severity of cerebral palsy using Mel-frequencies stochastics approach,” International Journal of Integrated Engineering, vol. 11, no. 3, 2019.
[33] D. Sangeetha, K. Dharani Dharan, A. C. Krishna, and C. Karthikeyan, “Speech and text conversion system for sign language using ML,” International Journal of Computer Technology and Electronics Communication (IJCTEC), vol. 9, no. 3, May–Jun. 2026, doi: 10.15680/IJCTECE.2026.0903003.
[34] B. Alabduallah, R. Al Dayil, A. Alkharashi, and A. A. Alneil, “Innovative hand pose based sign language recognition using hybrid metaheuristic optimization algorithms with deep learning model for hearing impaired persons,” Scientific Reports, vol. 15, Art. no. 9320, 2025, doi: 10.1038/s41598-025-93559-4.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Khalid Mohammed Osman Saeed, Bashier Suliman Elbashier Ahmed
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms.
