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Biometric data are data generated through a biometric process, including a person’s physiological and behavioral features. Biometric data are basically used to design systems for individual authentication and identification. People generally store their valuable data in laptops and mobile devices protected mainly by a password. However, people are not very careful about choosing or storing their passwords, making their valuable data insecure. By making use of biometric data, users can be identified more easily and securely.

Day by day, such authentication systems are increasing in popularity—one simple reason being that biometric data cannot be guessed like a password or user ID. Also, users are not bothered to remember or store carefully a password for authentication. Biometric data are divided into two categories: structural biometric and behavioral biometric.

Structural biometric data take into account the physiological or biological aspects of an individual to record structural biometric data. Examples of structural biometric data include iris, face, fingerprint, hand geometry, retina, vein, voice, and the like. Behavioral biometric data take into account mannerisms or the way a person does something—for example, one’s signature, walk/gait pattern, and keyboard typing. This entry provides a descriptive analysis of different types of biometric data, exploring individual authentication and identification aspects of human development.

Structural Biometric Data

Fingerprint Biometric Data

The most popular biometric data are fingerprint biometrics, used in the most commonly deployed models of user authentication. A fingerprint is basically a combination of ridges and furrows that remain on the surface of the fingertip. Ridges make up the continuous dark pattern flow in fingerprint, and furrows make up the light area between ridges. Fingerprint data are an impression of the abrasion ridges that are found on the inner surface of thumb and other fingers. These ridges follow a pattern—for example, twin loop, tented arch, right and left loop, whorl, and arch. Fingerprint data also include skin pore details.

All these features are global, but there are also a few local features or patterns, such as bifurcate, known as minutiae. There are a few biological principles of fingerprint data; for example, different fingers have different characteristics of epidermal ridges and furrows. Systematic classification is done because the configuration types vary individually but within certain limits. The slightest details and patterns of ridges and furrows are unchanging and permanent for each finger.

This technology has a number of strengths: Fingerprint scanning has a variety of applications such as e-commerce, system login, and physical access. It is a mature field now, and it also provides a high level of accuracy. The market for compact low-cost fingerprint capturing devices is growing. A number of smartphones come with a built-in fingerprint scanner—for example, HTC 10, Xiaomi Mi Max, and Samsung Galaxy Note 6. Finger scan technologies come with high reliability and ease of use. Some weaknesses of this technology are that users with a finger injury cannot make use of this technology either temporarily or permanently and its social acceptability is not as high as for some other technologies.

Voice Biometric Data

In voice biometrics, a voice print model is created by digitizing a person’s voice. It allows an individual to use his or her voice as an input to gain access to a device. Every individual’s voice is unique because of the unique resonance of the raw sound of an individual’s vocal cords in the chest, nasal cavities, mouth, larynx, and pharynx. Differentiation in voice also exists because of the way voice is formed by the tongue and throat and the way it radiates from the lips as well as its reflection from cheeks.

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