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Finger Print Biometric Identification and How It Works

By Edited Feb 20, 2016 0 0

Why Finger Print Biometrics?

Biometric devices (machines that process universally unique physical traits such as facial structure or retina) have been employed for years as a means to positively identify individuals for purposes such as access control. One of the most widely used forms of biometric identification is finger print scanning, because it provides a balance of convenience, user acceptability, and reliability that is difficult to attain with most other forms of biometric identification. Unlike other biometric systems in use today, fingerprint analysis techniques have been employed in areas such as law enforcement for more than a century, however capturing a digital fingerprint representation for biometric identification purposes is a task involving layers of complexity.

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Components of Finger Print Biometrics

Fingerprint biometric technology analyzes and records properties captured using a finger scanner, including the ridge lines and valleys in a fingerprint at global, local and micro levels. At the global level, places where ridge lines form high-curvature shapes (referred to as “singularities”) are the primary focus. Singularities are classified as either left or right loops, normal arch or tented arch deltas, or whorl singularities. At the local level, the point at which ridge lines are discontinuous are identified as “minutiae.” At the micro level, intra-ridge pores are identified, including pore density, position, shape and relative size. These aspects are captured using a finger scanner employing either optical (which uses light to capture the fingerprint) or solid-state (uses thermal, capacitive or electrical means for capture) sensors. While both sensor types can be configured for “sweep” motion scanning or touch type scanning, touch only is recommended as the sweep variety requires additional operator training and time per capture. Captured prints are simplified through three processes that include binarisation (conversion of the print image to black and white), thinning (reducing the size if the ridge width to one pixel), and minutiae detection. Once this data is stored it is used for matching during the authentication process using correlation (superimposing the stored template over a current image), or minutiae-based matching (which is used in most cases) in concert with ridge-based matching when minutiae are difficult to read.[1]

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  1. Arun Ross Information Fusion in Fingerprint Authentication. Lansing MI, PhD Dissertation: Michigan State University, 2009.

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