Evaluation of Rarity of Fingerprints in Forensics
Evaluation of Rarity of Fingerprints in Forensics
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发表时间：
2010-12
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通讯作者：
Chang Su;S. Srihari
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作者：
Chang Su;S. Srihari
给出了一种用细节点表示的潜在指纹稀有度的计算方法。它允许确定在n个已知指纹的数据库中找到证据指纹的匹配的概率。证据和数据库之间随机对应的概率通过三个程序步骤确定。在配准步骤中，通过寻找核心点来对齐潜在的指纹；这是使用基于基于高斯过程的机器学习方法的过程来完成的。在证据概率评估阶段，使用基于贝叶斯网络的产生式模型来确定证据的概率；该模型同时考虑了每个细节节点对附近细节节点的依赖程度以及它们在证据中存在的置信度。在随机对应的特定概率步骤中，使用证据概率来确定给定容差的n之间的匹配概率；最后的评估类似于特定生日的生日对应概率。生成模型通过使用标准指纹数据库评估的拟合度测试来验证。对于不同数量的细节节点，评估了几个潜在指纹的随机对应概率。
A method for computing the rarity of latent fingerprints represented by minutiae is given. It allows determining the probability of finding a match for an evidence print in a database of n known prints. The probability of random correspondence between evidence and database is determined in three procedural steps. In the registration step the latent print is aligned by finding its core point; which is done using a procedure based on a machine learning approach based on Gaussian processes. In the evidence probability evaluation step a generative model based on Bayesian networks is used to determine the probability of the evidence; it takes into account both the dependency of each minutia on nearby minutiae and the confidence of their presence in the evidence. In the specific probability of random correspondence step the evidence probability is used to determine the probability of match among n for a given tolerance; the last evaluation is similar to the birthday correspondence probability for a specific birthday. The generative model is validated using a goodness-of-fit test evaluated with a standard database of fingerprints. The probability of random correspondence for several latent fingerprints are evaluated for varying numbers of minutiae.