Performance Consistency of Fingerprints Across Different Sensors

Abstract: Large scale fingerprint recognitions implementations will move towards an architecture with centralized matching/storing subsystem and a decentralized accquisition subsystem. In such an implementation it is necessary to understand consistency issues of fingerprints collected from the same individual across different fingerprint sensors. This study is aimed at analysing consistency of image quality and matching performance rates for fingerprint samples collected from three different sensors.

Methodology: Three different fingerprint sensor were used to perform data collection
DigitalPerson U.are.U 4000 -optical sensor 500dpi
Identix DFR2090 -optical sensor 500dpi
Authentec AF-S2 -capacitance sensor 250dpi

6 fingerprint images from the right index finger were collected from 55 subjects on each of the three sensors.

A commercially available image quality software will be used to calculate image quality for the fingerprint samples. In order to test if the image quality is the same across the three different sensors, Kruskal -Wallis test will be performed.
VeriFinger 5.0 by Neurotechnologija will be used to measure False Non Match Rate (FNMR) and False Match Rate(FMR).

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Image Quality and Performance of Fingerprints Across Age Groups

Abstract: Fingerprint recognition systems are heavily influenced by the quality of samples provided to the system. Various factors like ability to interact with the sensor, wear and tear on surface of the finger, elasticity of the skin on finger etc. influence the quality of fingerprint samples. With deployments of fingerprint recognition systems that include users from all types of age groups an understanding of age on fingerprint image quality and performance rates is imperative. The focus of this research is to study the difference in image quality and performance rates across four different age groups: 18-25, 26-39, 40-62, 62 and above.

Methodology: DigitalPersona U.are.U 4000 optical sensor was used to collect the fingerprint images. Three fingerprint samples were collected from each finger in the dataset. There were unequal number of different fingers in each age group. Given below is the number of different fingers in each age group:
18-25: 158 different fingers
26-39: 48 different fingers
40-62: 52 different fingers
62 and above: 120 different fingers

A commercially available image quality algorithm was used to extract image quality score and minutiae count for the fingerprint samples.

The analysis will invovle performing non-parametric Kruskal-Wallis test for difference in image quality and minutiae count between the 4 groups. Post-hoc analysis will be performed if image quality and minutiae counts are found to be different across the age groups. The performance rates will invovled measure Fasle Non-Match Rates(FNMR) and False Match Rates(FMR).

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