Fingerprint recognition

What is fingerprint recognition?

Fingerprint recognition or fingerprint authentication refers to the automated method of verifying a match between two human fingerprints.

The analysis of fingerprints for matching purposes generally requires the comparison of several features of the print pattern. These include patterns, which are aggregate characteristics of ridges, and minutia points, which are unique features found within the patterns. It is also necessary to know the structure and properties of human skin in order to successfully employ some of the imaging technologies.

Fingerprint patterns

The three basic patterns of fingerprint ridges are the arch, loop, and whorl.
Scientists have found that family members often share the same general fingerprint patterns, leading to the belief that these patterns are inherited.

Fingerprint minutia features

The major Minutia features of fingerprint ridges are: ridge ending, bifurcation, and short ridge (or dot).

The ridge ending is the point at which a ridge terminates. Bifurcations are points at which a single ridge splits into two ridges. Short ridges (or dots) are ridges which are significantly shorter than the average ridge length on the fingerprint. Minutiae and patterns are very important in the analysis of fingerprints since no two fingers have been shown to be identical.

Multispectral Imaging – Seeing Beyond the Surface

Multispectral imaging is a sophisticated technology that was developed to overcome the fingerprint capture problems conventional imaging systems have in less-than-ideal conditions. The core problem was that conventional technologies rely on unobstructed and complete contact between the fingerprint and the sensor, a condition that is elusive in the real world.

The more effective solution was based on using multiple spectrums of light and advanced polarization techniques to extract unique fingerprint characteristics from both the surface and subsurface of the skin. The nature of human skin physiology is such that this subsurface information is both relevant to fingerprint capture and unaffected by surface wear and other environmental factors.

The technology allows to discriminate a real finger from an imposter or “spoof” fingerprint, making the sensor to be a leader in liveness detection. Multiple images of surface and subsurface fingerprint data is quickly acquired and reconstructed to produce a single high-quality fingerprint image.

How it works?

Multispectral imaging looks at and beyond the skin surface to the subsurface foundation of the fingerprint ridges. Different wavelengths of visible light interact with the skin in different ways, enabling significantly enhanced data capture. The fingerprint pattern on the surface echoes the subsurface structures from which they arose during development.

Multispectral imaging exploits the dependent relationship between surface and subsurface fingerprint patterns; subsurface data collected by multispectral imaging technology supports and augments surface data to create the highest-quality fingerprint image available.

Multiple images of surface and subsurface fingerprint data is quickly acquired and reconstructed to produce a single high-quality fingerprint image.

    •Multiple illumination wavelengths, angles, and polarizations
    •Acquire images of objects whether or not they are in contact with the sensor
    •Raw images are combined to reconstruct the fingerprint or other object

Capturing in Real-World conditions

The Multi-Spectral sensor succeeds to capture the fingerprint image in real world conditions:

• Wet conditions - Coastal/humid regions, sweaty fingers, outdoor rainy conditions

• Dirt conditions - dusty regions (in rural and urban areas), dirty fingers of factory workers, farmers, mechanics

• Dry conditions - desert, dry regions, dry fingers of Construction workers and farmers

• Variable pressure/contact - common with children, the elderly

• Bright light conditions - outdoor data collection under bright sunlight.