Enhancing Optical Cross-Sensor Fingerprint Matching Using Local Textural Features

Fingerprint systems have been designed to typically operate on images acquired using the same sensor. Existing fingerprint systems are not able to accurately compare images collected using different sensors. In this paper, we propose a learning-based scheme for enhancing interoperability between optical fingerprint sensors by compensating the output of a traditional commercial matcher. Specifically, cross-sensor differences are captured by incorporating Local Binary Patterns (LBP) and Local Phase Quantization (LPQ), while dimensionality reduction is performed by using Reconstruction Independent Component Analysis (RICA). The evaluation is carried out on rolled fingerprints pertaining to 494 users collected atWest Virginia University and acquired using multiple optical sensors and Ten Print cards. In cross-sensor at False Acceptance Rate of 0.01%, the proposed approach achieves a False Rejection Rate of 4.12%.