Multispectral Palmprint Encoding and Recognition
This work addresses the need for reliable and efficient human identification in biometrics, offering incremental improvements in accuracy and storage for palmprint recognition systems.
The authors tackled the problem of multispectral palmprint recognition by proposing a feature encoding scheme and binary hash table for efficient matching, achieving error rates of 0.003% on the PolyU dataset and 0.2% on the CASIA dataset, which are the lowest reported in literature.
Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.