Search

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). [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes an efficient indexing technique for fingerprint database using minutiae based geometric hashing. This technique consists of two stages, known as indexing and searching. For an accurate match at the time of searching, it has proposed a fixed length feature vector built from each minutia, known as Minutia Binary Pattern. Unlike any existing geometric based indexing technique, the proposed technique inserts each minutia along with the feature vector exactly once into a hash table. As a result, it reduces both computational and memory costs. Since minutiae of all fingerprint images in the database are found to be well distributed into the hash table, no rehashing is required. Experiments over FVC 2004 datasets prove the superiority of the proposed indexing technique against well known geometric based indexing techniques. [Copyright &y& Elsevier]

Abstract: Financial inclusion is the delivery of financial services at affordable cost to sections of disadvantaged and low income segments of society. It has several leakages such as security threats and transaction frauds in the available system. The use of biometrics in the financial inclusion is a near perfect solution to such security threats. Such a biometric system should be fast enough to recognise/identify a subject from a large database. This paper proposes an indexing technique for a biometric database consisting of variable number of features with high dimensions. The technique makes use of geometric properties of principal components of features. Unlike existing indexing techniques, it inserts fewer features into a hash table. It reduces both computational and memory costs significantly. The geometric properties of principal components of features are found to be robust to handle translation and rotation effects. The proposed technique has been tested on palmprint biometric databases viz. PolyU [25], CASIA [26] and IITK which have 7752, 5239 and 549 images respectively. It is found to be more efficient than any other existing indexing techniques. [Copyright &y& Elsevier]

Abstract: This paper proposes a hierarchical approach to retrieve an iris image efficiently from for a large iris database. This approach is a combination of both iris color and texture. Iris color is used for indexing and texture is used for retrieval of iris images from the indexed iris database. An index which is determined from the iris color is used to filter out the images that are not similar to the query image in color. Further, iris texture features of those filtered images, are used to determine the images which are similar to the query image. The iris color information helps to design an efficient indexing scheme based on color indices. The color indices are computed by averaging the intensity values of all red and blue color pixels. Kd-tree is used for real-time indexing based on color indices. The iris texture features are obtained through Speeded Up Robust Features (SURF) algorithm. These features are used to get the query’s corresponding image at the top best match. The performance of the proposed indexing scheme is compared with two well known iris indexing schemes () on UPOL () and UBIRIS () iris databases. It is observed that combination of iris color and texture improves the performance of iris recognition system. [Copyright &y& Elsevier]