Michigan State University, East Lansing, MI 48824, USA.
IEEE Trans Pattern Anal Mach Intell. 2013 Oct;35(10):2307-22. doi: 10.1109/TPAMI.2013.39.
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.
随着实时扫描掌纹技术的出现，高分辨率掌纹识别开始在法医学和执法领域受到广泛关注。在法医学应用中，潜伏掌纹提供了关键证据，据估计，大约 30%在犯罪现场发现的潜伏掌纹都是手掌的。大多数现有的高分辨率掌纹匹配算法基本上都遵循基于细节点的指纹匹配策略。考虑到大量的细节点（与滚动指纹相比，全掌纹中大约有 1000 个细节点，而全掌纹中大约有 1000 个细节点）和大面积的前景区域，需要开发新的策略来实现高效和稳健的潜伏掌纹匹配。在本文中，我们专门为掌纹匹配设计了一种基于细节点聚类和细节点匹配传播的粗到精匹配策略。为了处理大量的细节点，我们设计了一种基于局部特征的细节点聚类算法，将细节点聚类成几个组，使得属于同一组的细节点具有相似的局部特征。然后在每个组内进行粗匹配，在两幅掌纹之间建立初始细节点对应关系。从每个初始对应关系开始，细节点匹配传播算法在全掌纹中搜索匹配的细节点。我们提出的掌纹匹配算法已经在一个由 446 个潜伏掌纹和 12489 个背景全掌纹组成的潜伏到全掌纹数据库上进行了评估。匹配结果表明，在同一潜伏数据库上，与一种最先进的潜伏掌纹匹配算法相比，该算法的识别准确率为 79.4%，排名第一，明显高于 60.8%。在 Windows XP 台式系统上，我们的算法对单个潜伏到全匹配的平均计算时间约为 141ms（真实匹配）和 50ms（伪造匹配），该系统的 CPU 为 2.2GHz，RAM 为 1.00GB。我们算法的计算时间比之前发表的最先进算法快一个数量级。
