Advancements in Hand Recognition Systems: Challenges and Future Directions
Jinwoo Park Sunghee, Hyejin Lee Ok, Sungmin Kang · Preprints.org · 2024
Hand recognition is a rapidly evolving field in biometric identification, offering unique advantages for secure, contactless authentication across diverse applications. This paper provides a comprehensive overview of current hand recognition technologies, including palmprint, hand geometry, and vein pattern recognition, and explores emerging trends in multimodal systems, artificial intelligence, and sensor innovations. We examine critical challenges such as environmental variability, computational demands, and privacy concerns, which impact the performance and acceptance of hand recognition systems. Additionally, we discuss future directions, including the integration of advanced deep learning techniques, privacy-preserving methods like federated learning and homomorphic encryption, and the potential use of blockchain for secure data management. The paper highlights how developments in edge computing and hardware improvements will enhance real-time processing and accessibility of hand recognition on resource-constrained devices. By addressing these challenges and embracing innovations, hand recognition technology is expected to play a transformative role in sectors ranging from security and healthcare to smart cities and autonomous systems. The findings aim to guide researchers and developers in advancing robust, secure, and ethical hand recognition systems for a wide range of applications.