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]

Face stands out as a preferable biometric trait for automatic human authentication as it is intuitive and non-intrusive. This paper investigates various feature-based automatic face recognition approaches in detail. High degree of freedom in head movement and human emotion leads a face recognition system to face critical challenges in terms of pose, illumination and expression. Human face also undergoes irreversible changes due to aging. These factors makes the process of face recognition non trivial and hard. This paper also provides a review of the facial recognition approaches individually dealing with these issues. Applications of face recognition in the forensic domain sometimes needs identification using a scanned facial image. The scenario is quite useful to get investigative leads. Important approaches for the same are also been discussed in the manuscript. Recent developments in the low-cost image capturing devices has flooded the facial image databases with a lot of images, at the same time availability of GPU based compute power has helped develop deep learning approaches to handle the face recognition at a very accurate and massive level. The same has also been surveyed and analyzed in the manuscript. [ABSTRACT FROM AUTHOR]

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]