Roja Lakshmi Sahoo supervised by Dr. Anoop Namboodiri received her Master of Science – Dual Degree in Computer Science and Engineering (CSD). Here’s a summary of her research work on Flash–Non-Flash Fusion for Contactless Fingerprint Recognition and Spoof Detection

Contactless fingerprint recognition provides a number of benefits in terms of hygiene and minimizing sensor-induced artifacts, over traditional contact-based systems, but it is also associated with a number of challenges that are currently hindering its adoption. The 2D representation of 3D features, in combination with unconstrained illumination, affects the quality of ridges, resulting in inconsistent features. Also, the lack of interaction information in contactless recognition makes it more prone to presentation attacks. Current techniques for acquisition, enhancement, and detection of spoofing attacks are mostly independent and focused on single images, without exploiting any complementary information for improved recognition. As the trend towards contactless biometric recognition is becoming increasingly necessary for usability and cost-effectiveness in terms of deployment in mobile and security-critical applications, lower matching accuracy and increased susceptibility to spoofing attacks are significant challenges. This highlights the need for a method that jointly addresses enhancement, recognition, and spoof detection while improving cross-modal interoperability. In our work, we present a unified framework for contactless fingerprint recognition by exploiting the flash–non-flash acquisition pair as a lightweight active sensing mechanism that extracts complementary illumination information. We thus present the Flash–Non-Flash Fingerphoto (FNF) Database for a thorough analysis of the illumination impact, and Fusion2Print (F2P), an end-to-end deep learning framework that learns the optimal enhancement in the spatial, frequency, and color domains to enhance the quality of the ridge-valley representations and cross-domain recognition accuracy. Furthermore, we investigate a method for the detection of presentation attacks by exploiting the illumination-dependent photometric differences between genuine and spoofed fingerprints, and a low-cost 3D pipeline for the reconstruction of the fingerprint surface geometry from just two images, facilitating the simulation of digital spoof attacks. Experimental results show that all components benefit from improvements. F2P obtains AUC of 0.999 and EER of 1.12%. The illumination-aware technique separates real and spoofing samples effectively, and the 3D reconstruction pipeline rebuilds finger geometry and ridge-valley information with improved contrast. These components constitute a refined unified framework for contactless fingerprint recognition. 

June 2026