Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2014 | CoRR | Computer vision,Pattern recognition,Identifier,Computer science,Source code,Multispectral image,Artificial intelligence,Biometrics,Encoding (memory),Binary number,Hash table |
DocType | Volume | Citations |
Journal | abs/1402.2941 | 2 |
PageRank | References | Authors |
0.37 | 23 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zohaib Khan | 1 | 45 | 5.56 |
| Faisal Shafait | 2 | 1324 | 88.97 |
| Yiqun Hu | 3 | 807 | 38.45 |
| A. Mian | 4 | 1679 | 84.89 |