Statistical quality assessment of a fingerprint

Kyungtae Hwang

doi:10.1117/12.541013

25 August 2004 Statistical quality assessment of a fingerprint

Kyungtae Hwang

Proceedings Volume 5404, Biometric Technology for Human Identification; (2004) https://doi.org/10.1117/12.541013
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

The quality of a fingerprint is essential to the performance of AFIS (Automatic Fingerprint Identification System). Such a quality may be classified by clarity and regularity of ridge-valley structures.^1,2 One may calculate thickness of ridge and valley to measure the clarity and regularity. However, calculating a thickness is not feasible in a poor quality image, especially, severely damaged images that contain broken ridges (or valleys). In order to overcome such a difficulty, the proposed approach employs the statistical properties in a local block, which involve the mean and spread of the thickness of both ridge and valley. The mean value is used for determining whether a fingerprint is wet or dry. For example, the black pixels are dominant if a fingerprint is wet, the average thickness of ridge is larger than one of valley, and vice versa on a dry fingerprint. In addition, a standard deviation is used for determining severity of damage. In this study, the quality is divided into three categories based on two statistical properties mentioned above: wet, good, and dry. The number of low quality blocks is used to measure a global quality of fingerprint. In addition, a distribution of poor blocks is also measured using Euclidean distances between groups of poor blocks. With this scheme, locally condensed poor blocks decreases the overall quality of an image. Experimental results on the fingerprint images captured by optical devices as well as by a rolling method show the wet and dry parts of image were successfully captured. Enhancing an image by employing morphology techniques that modifying the detected poor quality blocks is illustrated in section 3. However, more work needs to be done on designing a scheme to incorporate the number of poor blocks and their distributions for a global quality.

Citation Download Citation

Kyungtae Hwang "Statistical quality assessment of a fingerprint", Proc. SPIE 5404, Biometric Technology for Human Identification, (25 August 2004); https://doi.org/10.1117/12.541013

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KEYWORDS

Image quality

Distance measurement

Optical components

Quality measurement

Digital filtering

Fingerprint recognition

Image enhancement

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