Contactless Palmprint

Contactless palmprint recognition aims to identify individuals using images of their palms captured without physical contact, offering a hygienic and user-friendly biometric authentication method. Current research focuses on improving accuracy and speed through the development of sophisticated deep learning models, such as convolutional neural networks and vision transformers, often combined with techniques to enhance image quality and efficiently represent palmprint features. This technology shows promise for applications ranging from secure access control to addressing challenges in identifying children in resource-limited settings where reliable identification documents are scarce, particularly for distributing healthcare and aid. The development of synthetic palmprint datasets using diffusion models is also a key area of focus to overcome limitations posed by the scarcity of real-world data.