| Title | A Study of Temporal Stability on Finger-Vein Recognition Accuracy Using a Steady-State Model |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Liu, Shilei, Xu, Guoxiong, Zhang, Yi, Li, Wenxin |
| Conference Name | Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-6366-2 |
| Keywords | A steady-state model, Bio-feature consistency/persistence, Biometric recognition & verification, BIOS Security, Finger-vein, Human Behavior, Metrics, pubcrawl, Resiliency, Scalability |
| Abstract | Stability has been one of the most fundamental premises in biometric recognition field. In the last few years, a few achievements have been made on proving this theoretical premises concerning fingerprints, palm prints, iris, face, etc. However, none of related academic results have been published on finger-vein recognition so far. In this paper, we try to study on the stability of finger-vein within a designed timespan (four years). In order to achieve this goal, a proper database for stability was collected with all external influences of finger-vein features (acquiring hardware, user behavior and circumstance situation) eliminated. Then, for the first time, we proposed a steady-state model of finger-vein features indicating that each specific finger owns a stable steady-state which all its finger-vein images would properly converging to, regardless of time. Experiments have been conducted on our 5-year/200,000-finger data set. And results from both genuine match and imposter match demonstrate that the model is well supported. This steady-state model is generic, hence providing a common method on how to evaluate the stability of other types of biometric features. |
| URL | http://doi.acm.org/10.1145/3232059.3232070 |
| DOI | 10.1145/3232059.3232070 |
| Citation Key | liu_study_2018 |
A Study of Temporal Stability on Finger-Vein Recognition Accuracy Using a Steady-State Model