The problem of pursuing the data items of a large database whose distances to a query item are the least is known as Similarity Search (Nearest Neighbor Search) problem. There exist various algorithms to address this problem. Some of the well known algorithms are i) exact algorithms ii) approximation algorithms and iii) randomized algorithms. This paper has made study only on exact and approximation algorithms because randomized algorithm produces approximate results with some probability. Recently, there are several approximation algorithms are proposed by the researchers because this type of algorithms minimizes the problem of Curse of Dimensionality.

This paper mainly has two major sections. In first section, various methods under exact and approximation algorithms are discussed with regard to storage, preprocessing and query time. In the second section, efficient algorithms for similarity search suitable for certain physiological characteristics based biometric systems are considered. Biometric system has five main steps viz acquisition of Image, pre-processing, extraction of features, matching and making final decision. In this paper, indexing algorithms for similarity search suitable for iris trait based on different features are discussed in detail. Since the nature of features are distinct and different in biometric traits, there does not exist a universal (one unique) solution which can apply to all traits of biometric systems. Various performance measures like Penetration Rate and Hit Rate are used to determine the correct recognition rate with top best match (rank-1 accuracy).

追求距查询项的距离最小的大型数据库的数据项的问题称为相似性搜索（最近邻居搜索）问题。存在各种算法来解决该问题。一些众所周知的算法是：i）精确算法ii）近似算法和iii）随机算法。本文仅对精确算法和近似算法进行了研究，因为随机算法以一定的概率产生近似结果。最近，研究人员提出了几种近似算法，因为这种算法可以最大程度地减少维数诅咒的问题。

本文主要有两个主要部分。在第一部分中，针对存储，预处理和查询时间，讨论了精确算法和近似算法下的各种方法。在第二部分中，考虑了适用于基于某些生理特征的生物识别系统的有效相似性搜索算法。生物识别系统有五个主要步骤，即图像获取，预处理，特征提取，匹配和做出最终决定。在本文中，详细讨论了基于不同特征的适合虹膜特征的相似性搜索索引算法。由于特征的性质在生物特征方面是独特且不同的，因此不存在可适用于生物特征系统所有特征的通用（唯一）解决方案。各种性能指标，例如渗透率和命中率用于确定最佳匹配度（等级1精度）的正确识别率。