• Complain

Bhagvati Chakravarthy. - Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems

Here you can read online Bhagvati Chakravarthy. - Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. City: Cham, year: 2017, publisher: Springer International Publishing : Imprint : Springer, genre: Home and family. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:

Romance novel Science fiction Adventure Detective Science History Home and family Prose Art Politics Computer Non-fiction Religion Business Children Humor

Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.

Bhagvati Chakravarthy. Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems

Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems: summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

This work presents a review of different indexing techniques designed to enhance the speed and efficiency of searches over large biometric databases. The coverage includes an extended Delaunay triangulation-based approach for fingerprint biometrics, involving a classification based on the type of minutiae at the vertices of each triangle. This classification is demonstrated to provide improved partitioning of the database, leading to a significant decrease in the number of potential matches during identification. This discussion is then followed by a description of a second indexing technique, which sorts biometric images based on match scores calculated against a set of pre-selected sample images, resulting in a rapid search regardless of the size of the database. The text also examines a novel clustering-based approach to indexing with decision-level fusion, using an adaptive clustering algorithm to compute a set of clusters represented by a leader image, and then determining the index code from the set of leaders. This is shown to improve identification performance while using minimal resources.;Introduction -- Hierarchical Decomposition of Extended Triangulation for Fingerprint Indexing -- An Efficient Score-Based Indexing Technique for Fast Palmprint Retrieval -- A New Cluster-Based Indexing Technique for Palmprint Databases Using Scores and Decision-Level Fusion -- Conclusions and Future Scope.

Bhagvati Chakravarthy.: author's other books


Who wrote Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems? Find out the surname, the name of the author of the book and a list of all author's works by series.

Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems — read online for free the complete book (whole text) full work

Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.

Light

Font size:

Reset

Interval:

Bookmark:

Make
The Author(s) 2017
Ilaiah Kavati , Munaga V.N.K. Prasad and Chakravarthy Bhagvati Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems SpringerBriefs in Computer Science 10.1007/978-3-319-57660-2_1
1. Introduction
Ilaiah Kavati 1 , Munaga V. N. K. Prasad 2 and Chakravarthy Bhagvati 3
(1)
MLR Institute of Technology, Hyderabad, Andhra Pradesh, India
(2)
Institute for Development and Research in Banking Technology, Hyderabad, Andhra Pradesh, India
(3)
University of Hyderabad, Hyderabad, Andhra Pradesh, India
Ilaiah Kavati
Email:
Abstract
In biometric identification systems, the identity corresponding to an individual is determined by comparing his/her template against all user templates in the database. This exhaustive matching process increases the response time and the number of false matches of the system. An effective mechanism is required that reduces the number of templates to be compared with the query during identification. Biometric indexing is such technique that limits the search space and identifies an individual in real time with high accuracy. Many authors have presented a number of biometric indexing techniques. This chapter explores the fundamentals of biometric indexing, its challenges, classifying and benchmarking along with a number of techniques proposed by various researchers.
Keywords
Biometrics Verification Identification Indexing Classification
1.1 Introduction
In todays security conscious society, automatic personal authentication is important in different applications including government, commercial, educational institutions, industries, public places, etc. Questions such as Is this the person who he claims to be?, Should this individual be authorized to perform this transaction?, Does this employee have authorization to access this service? etc., are asked millions of time every day by thousands of organizations in both private and public sectors [].
Existing systems use either identity cards or passwords for personal authentication (Fig. a). These security systems no longer suffice for individual authentication because cards can be stolen or forged and a password can be forgotten or cracked. The following are some interesting statistics:
  1. According to a report by Nilson, $11.27 billion losses due to credit card and debit card fraud during 2012 [].
  2. According to American Bankers Associations Deposit Account Fraud Survey-2011, Financial institutions incurred $955 million in losses due to debit card fraud in 2010, which is around a 21% increase from the $788 million in losses incurred during 2008 [].
  3. According to the Gartner Group, between 20 to 50% of all help desk calls are for password resets and the average help desk labor cost for a single password reset is about $70 [].
The above statistics shows the need of an accurate and efficient approach for personal recognition. Biometric recognition that uses humans fingerprint and/or palmprint and/or iris, etc., is a better choice and a reliable solution for convenient human recognition (Fig. ], recognition in these large biometric systems is a challenging problem. In this book, we explore methods that are capable of searching biometric databases in real time with a high level of confidence.
Fig 11 Personal authentication techniques a Traditional methods such as - photo 1
Fig. 1.1
Personal authentication techniques: a Traditional methods such as identity cards, Passwords, etc., b Biometric characteristics []
1.2 Biometric Recognition
A biometric system is a pattern recognition system that recognizes individuals based on the measurement of their physiological and/or behavioral traits: Physiological traits include a persons fingerprint, facial features, palmprint, vein pattern, or ocular characteristics; Behavioral traits include voice, gait, keystrokes, signature etc. [ shows a few of the biometric traits (including physiological and behavioral) for personal recognition.
A generic biometric system is shown in Fig.. It consists of two modules: enrollment and recognition.
Enrollment
This module enrolls the individuals into the biometric system (Fig. a). During enrollment, a sensor captures the biometric characteristic of an individual, from which a set of features (template) are extracted by a feature extractor. Depending on the application context, the extracted feature template may be stored in a central database along with the individuals identity (name, ID number, etc.) or be recorded on a smart card issued to the individual.
Recognition
This module recognizes the identity of an individual at the point of service. During this phase, the sensor acquires the biometric characteristic of the individual to be recognized. The captured biometric image is preprocessed by the feature extractor to generate the template. The extracted template is compared to the prestored template(s) using a matcher to establish the identity. The process of user recognition in biometric systems is shown in Fig. b, c. A biometric recognition system is designed to work in one of the two different modes: (i) verification or (ii) identification.
Fig 12 Different biometric traits for personal recognition 121 - photo 2
Fig. 1.2
Different biometric traits for personal recognition
1.2.1 Verification
In verification mode, the user will claim his identity by using a user name, or a personal identification number, or a smart card, etc., along with the biometric data. The system will then verify the user by matching the acquired biometric characteristic with his own biometric sample prestored in the system. The system in this mode, conducts a one-to-one matching to determine whether the identity claimed by the individual is true or not [b.
1.2.2 Identification
In this mode, the user does not claim any identity. The user provides his biometric data, and the data is compared to the stored template of every individual in the system database. The system in this mode, conducts a one-to-many comparison to find the identity of an individual. In this case, the question To whom does the submitted biometric data belong? is answered. For example, if a fingerprint impression is found at a crime scene, to determine the suspect it is compared to all the enrolled fingerprints in the database. If a match is found, the identity of the suspect is determined. The process of recognizing a user in identification mode can be seen in Fig. c.
Fig 13 Different modes of operation of a generic biometric system - photo 3
Fig. 1.3
Different modes of operation of a generic biometric system []
1.3 Indexing
In todays security conscious society, biometric recognition systems became more popular and deployed in variety of applications such as surveillance, border control, network access, banking, employee authentication, etc. The market for biometric applications is growing worldwide, and specifically in emerging economies, such as India, where scalability is a huge challenge. According to a market research report by Acuity Market Intelligence (AMI) [.
Note that, most of these biometric systems deal with large-scale databases and their size is increasing at a rapid pace. For instance, Indias national ID program []. Currently, it has records of over 51 million criminals and over 1.5 million noncriminals.
Next page
Light

Font size:

Reset

Interval:

Bookmark:

Make

Similar books «Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems»

Look at similar books to Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.


Reviews about «Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems»

Discussion, reviews of the book Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.