A Coarse to Fine Minutiae-Based Latent Palmprint Matching
文献类型：期刊论文
| 作者 | Liu, Eryun1,2; Jain, Anil K.1,3; Tian, Jie2,4
|
| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
 |
| 出版日期 | 2013-10-01 |
| 卷号 | 35期号:10页码:2307-2322 |
| 关键词 | Palmprint latent palmprint matching minutiae clustering minutia descriptor match propagation |
| 英文摘要 | With the availability of live-scan palmprint technology, high resolution palmprint recognition has started to receive significant attention in forensics and law enforcement. In forensic applications, latent palmprints provide critical evidence as it is estimated that about 30 percent of the latents recovered at crime scenes are those of palms. Most of the available high-resolution palmprint matching algorithms essentially follow the minutiae-based fingerprint matching strategy. Considering the large number of minutiae (about 1,000 minutiae in a full palmprint compared to about 100 minutiae in a rolled fingerprint) and large area of foreground region in full palmprints, novel strategies need to be developed for efficient and robust latent palmprint matching. In this paper, a coarse to fine matching strategy based on minutiae clustering and minutiae match propagation is designed specifically for palmprint matching. To deal with the large number of minutiae, a local feature-based minutiae clustering algorithm is designed to cluster minutiae into several groups such that minutiae belonging to the same group have similar local characteristics. The coarse matching is then performed within each cluster to establish initial minutiae correspondences between two palmprints. Starting with each initial correspondence, a minutiae match propagation algorithm searches for mated minutiae in the full palmprint. The proposed palmprint matching algorithm has been evaluated on a latent-to-full palmprint database consisting of 446 latents and 12,489 background full prints. The matching results show a rank-1 identification accuracy of 79.4 percent, which is significantly higher than the 60.8 percent identification accuracy of a state-of-the-art latent palmprint matching algorithm on the same latent database. The average computation time of our algorithm for a single latent-to-full match is about 141 ms for genuine match and 50 ms for impostor match, on a Windows XP desktop system with 2.2-GHz CPU and 1.00-GB RAM. The computation time of our algorithm is an order of magnitude faster than a previously published state-of-the-art-algorithm. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | PROPAGATION ; SIMILARITY |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000323175200001 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/4067]  |
| 专题 | 自动化研究所_中国科学院分子影像重点实验室 |
| 作者单位 | 1.Michigan State Univ, Dept Comp Sci & Engn, E Lansing, MI 48824 USA 2.Xidian Univ, Sch Life Sci & Technol, Xian 710126, Shaanxi, Peoples R China 3.Korea Univ, Dept Brain & Cognit Engn, Seoul, South Korea 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Eryun,Jain, Anil K.,Tian, Jie. A Coarse to Fine Minutiae-Based Latent Palmprint Matching[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2013,35(10):2307-2322. |
| APA | Liu, Eryun,Jain, Anil K.,&Tian, Jie.(2013).A Coarse to Fine Minutiae-Based Latent Palmprint Matching.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,35(10),2307-2322. |
| MLA | Liu, Eryun,et al."A Coarse to Fine Minutiae-Based Latent Palmprint Matching".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 35.10(2013):2307-2322. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。