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What is Fingerprint Identification?

What is Fingerprint Identification?

The mass adoption of fingerprint identification as a key for unlocking access to protected consumer electronics has grown in popularity. Fingerprints continue to represent one of the most common forms of biometrics people encounter on a daily basis. Used in forensics labs since the 1800s, the method has become digitized and automated since the 1960s, and the technology is now embedded in consumer smartphones and laptops for convenient and secure access control. Fingerprint biometrics also remain the foundation of many civil registries and national ID systems, border control processes and enterprise systems.

Fingerprints are virtually unique to every individual, consistent across a lifetime and are mostly ubiquitous. These factors make fingerprint identification a useful biometric for identifying a person, as it is incredibly unlikely to duplicated, have a false positive or brute forced. The “minutiae” – a pattern of ridges and valleys in a finger – are the most notable and distinct feature of a fingerprint, which makes them ideal to be captured in fingerprint identification.

To scan minutiae, optical, capacitive, ultrasound, and thermal scanners are employed. An optical scan essentially photographs an image of a person’s fingerprint for comparison to a stored template. A capacitive scan utilizes capacitors that store an electric charge and records the details of a fingerprint. Capacitive scans capture the path of electronic signals across the finger to measure features. Ultrasound scans emit a high frequency wave that is sent out to a finger and returned with details on the minutiae finger, similar to sonar. Thermal scanners detect the difference in temperature in the ridges and valleys of a fingerprint and creates an image based on the differences in heat, as ridges emanate more heat than valleys. All of the above methods are based on contact with a surface, called a paten, and until relatively recently, only contact-based methods of fingerprint capture were available.

Contactless fingerprint scanning has also been developed, using both dedicated devices and smartphone cameras.  The former may generate 3D fingerprint images during template creation, while the latter generates templates based on 2D images. The benefit of being more hygienic is notable, particularly with the COVID-19 pandemic, though reports by the National Institute of Standards and Technology (NIST) have confirmed that fingerprints captured with contactless systems can be compared with those enrolled using contact scanners, but also that accuracy with contactless scans typically remains lower than with contact scans.

Newer analytical techniques such as machine learning and deep neural networks have also been applied to minutiae extraction, template matching, liveness detection and image quality analysis, further advancing the field.

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