Download Biometrics in a Data Driven World: Trends, Technologies, and by Sinjini Mitra, Mikhail Gofman PDF

By Sinjini Mitra, Mikhail Gofman

Biometrics in a knowledge pushed international: traits, applied sciences, and Challenges goals to notify readers concerning the sleek functions of biometrics within the context of a data-driven society, to familiarize them with the wealthy background of biometrics, and to supply them with a glimpse into the way forward for biometrics.

The first component to the publication discusses the basics of biometrics and gives an outline of universal biometric modalities, particularly face, fingerprints, iris, and voice. It additionally discusses the historical past of the sector, and gives an outline of rising tendencies and possibilities. the second one component to the publication introduces readers to quite a lot of biometric purposes.

The subsequent a part of the publication is devoted to the dialogue of case reviews of biometric modalities presently used on cellular purposes. As smartphones and pill pcs are quickly turning into the dominant shopper desktop structures, biometrics-based authentication is rising as an essential component of shielding cellular units opposed to unauthorized entry, whereas allowing new and hugely renowned purposes, comparable to safe on-line cost authorization.

The booklet concludes with a dialogue of destiny traits and possibilities within the box of biometrics, in an effort to pave the best way for advancing learn within the sector of biometrics, and for the deployment of biometric applied sciences in real-world applications.

The ebook is designed for people attracted to exploring the modern functions of biometrics, from scholars to researchers and practitioners operating during this box. either undergraduate and graduate scholars enrolled in college-level defense classes also will locate this booklet to be an extremely valuable companion.

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Biometrics in a Data Driven World: Trends, Technologies, and Challenges

Biometrics in a knowledge pushed global: developments, applied sciences, and demanding situations goals to notify readers concerning the glossy functions of biometrics within the context of a data-driven society, to familiarize them with the wealthy background of biometrics, and to supply them with a glimpse into the way forward for biometrics.

Extra resources for Biometrics in a Data Driven World: Trends, Technologies, and Challenges

Example text

If one is stressed, he or she tends to speak faster, and if he or she is not tired, the speed tends to decrease. Finally, regional dialects involve widely different types of pronunciation, vocabulary, and grammar, which vary with the geographical area where the speaker comes from. Social dialects are distinguished by characteristics of pronunciation, vocabulary, and grammar according to the social group of the speaker. 5 Other Challenges and Issues Here, we summarize some of the other important challenges and issues with biometrics-based authentication.

A typical biometric system has the following three components: 1. Enrollment. A biometric image is captured by some device (known as the “sensor”), preprocessed for feature extraction where features denote the identifying characteristics that are used in the identification process, and enrolled in the system. The enrolled image is matched against the database of features extracted earlier from existing images, typically referred to as reference feature vectors. A decision is made about whether the person is genuine or an impostor.

Speaker recognition also suffers from several challenges, the primary one being background noise and other external factors that potentially affect the quality of the captured voiceprint. 6 shows sample voiceprint. , single fingerprint or face). A fingerprint image with a scar or a voice sample altered by cold is an example of noisy data. , poor illumination of a user’s face for face recognition). • Intraclass variations. , incorrect facial pose). In biometric systems comprised of a large number of users (say, at airports), there may be interclass similarities (overlap) in the feature space of multiple users.

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