Trustworthy AI in Biometrics: A Comprehensive Review of Trends and Challenges

Authors

  • Shefali Arora Department of Computer Science and Engineering, Dr BR Ambedkar National Institute of Technology, Jalandhar, India
  • Kanu Goel Department of Computer Science and Engineering, Punjab Engineering College, Chandgarh, India
  • Shikha Gupta Department of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India
  • Ruchi Mittal Iconic Data, Japan
  • Avinash Kumar Shrivastava Dept. of Quantitative Techniques & OM, International Management Institute, Kolkata, India

DOI:

https://doi.org/10.13052/jgeu0975-1416.1315

Keywords:

Trustworthiness, bias, explainability, biometrics, security, performance

Abstract

Deep Learning (ML) and Artificial Intelligence (AI) are rapidly spreading over many application domains. However, the creation of intelligent systems is constrained by inherent flaws in the learning algorithms that are employed. One major barrier to the application of these techniques is the unpredictable nature of model performance. The reliability of a model is determined by its capacity to remove biases, elucidate findings, and adapt to changes in input data. The idea of trustworthy AI uses a variety of machine learning techniques to explain a model’s decision-making process, hence boosting user confidence in the model’s output. The significance of reliable AI in the context of biometrics is the main topic of this study. Our survey identifies the several types of reliable AI that can be used to increase the dependability of the choices made.

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Author Biographies

Shefali Arora, Department of Computer Science and Engineering, Dr BR Ambedkar National Institute of Technology, Jalandhar, India

Shefali Arora is Assistant Professor in the Department of Computer Science and Engineering at National Institute of Technology Jalandhar. She completed her PhD from Netaji Subhas Institute of Technology Delhi, Masters from Thapar University Patiala and BTech from Indira Gandhi Institute Of Technology Delhi. Here research domain is Deep Learning, Computer Vision and Security. She has published papers in many renowned international conferences and prestigious journals.

Kanu Goel, Department of Computer Science and Engineering, Punjab Engineering College, Chandgarh, India

Kanu Goel is currently working as Assistant Professor in the Department of Computer Science & Engineering, Punjab Engineering College, Chandigarh. She holds a B.E. (Computer Engineering), M.E. (Computer Science and Engineering) and Ph.D., all from Thapar Institute of Engineering & Technology, Patiala. She received a Gold Medal for securing top position in M.E. in Computer Science and Engineering from Thapar University, Patiala in 2016. Her areas of research interests include Machine Learning, Artificial Intelligence, Concept Drift analysis. She has SCIE/Scopus indexed publications in various international journals and conferences.

Shikha Gupta, Department of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India

Shikha Gupta has worked as an Assistant Professor at Maharaja Agrasen Institute of Technology Delhi. Her area of expertise is Machine Learning, Data Analytics. She completed her PhD from Netaji Subhas Institute of Technology Delhi.

Ruchi Mittal, Iconic Data, Japan

Ruchi Mittal is a seasoned AI/ML professional with over 15 years of experience, specializing in Generative AI, Large Language Models (LLMs), and Natural Language Processing (NLP). With a Ph.D. in Computer Science from the University of Delhi, she has a proven track record of designing, fine-tuning, and deploying advanced AI models, including GPT, BERT, and LLaMA, for applications such as financial forecasting, sentiment analysis, and speech synthesis. Ruchi has published 40+ research papers in SCI, ABDC, and Scopus-indexed journals, focusing on AI/ML, NLP, and computer vision. Currently serving as a Senior Data Scientist-Lead at Iconic Data, Japan, she leads Generative AI projects, optimizes LLMs, and builds scalable AI pipelines. Ruchi is passionate about driving innovation through AI and mentoring teams to achieve technical excellence.

Avinash Kumar Shrivastava, Dept. of Quantitative Techniques & OM, International Management Institute, Kolkata, India

Avinash Kumar Shrivastava did B.Sc (H) in Mathematics and received his Master’s, M.Phil and PhD degrees in Operational Research from the Department of Operational Research, University of Delhi. His current teaching interest includes courses on decision sciences viz. Business Mathematics & Statistics, Operations Research, Quantitative Techniques, Multi-Criteria Decision Making (MCDM) and Data Analytics. He has presented papers at conferences of International repute and won accolades for best paper presentations. He has been publishing extensively and serving as a reviewer for various journals of International repute. He has edited seven books published by Bloomsbury Publications & Taylor & Francis. He is the series editor of a book titled “Advances in emerging markets and Business operations”. He is the managing editor of the International Journal of System Assurance Engineering and Management (IJSAEM), Springer and associate editor of IMI Konnect. He has organized International conferences & seminars in different capacities. He is also a life member of the Society for Reliability, Engineering, Quality and Operations Management (SREQOM).

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Published

2025-02-18

How to Cite

Arora, S., Goel, K., Gupta, S., Mittal, R., & Shrivastava, A. K. (2025). Trustworthy AI in Biometrics: A Comprehensive Review of Trends and Challenges. Journal of Graphic Era University, 13(01), 91–118. https://doi.org/10.13052/jgeu0975-1416.1315

Issue

2025: Vol 13 Iss 1

Section

Articles