A new approach for classification of fingerprint image quality

Wu, Jun; Xie, Shan Juan; Seo, Dong-Hun; Lee, Won Don

doi:10.1109/coginf.2008.4639191

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…Fingerprint quality has been the subject of research for the last several years. During this time, researchers have developed their own algorithms for assessing the quality of fingerprints such as [3], [2], [20], and [21]. On the other side, other researchers have looked into using already available software fingerprint quality such as with [3].…”

Section: Introductionmentioning

confidence: 99%

The Assessment of Fingerprint Quality for a More Effective Match Score in Minutiae-Based Matching Performers

Meyers¹

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One of the most common types of evidence recovered from a crime scene are latent fingerprints, however these impressions are often of low quality. The quality of a latent fingerprint is described as the degree to which the ridge details can be observed. If the quality of the latent fingerprint is very clear, a minutiae-based matching algorithm with automatic extraction may detect and utilize the minutiae that are truly present in the fingerprint. However, if the impression is of poor quality, the minutiae-based matching algorithm's automatic extraction may detect fewer features and could completely miss features resulting in the return of an unrelated candidate. The aim of this research was to determine a method to improve the match score of latent fingerprints by removing the bad quality regions, where both a subjective and objective methods were utilized. The subjective method utilized the predetermined quality categories of "good," "bad" or "ugly" to assign a latent fingerprint. After classification, each impression was processed by Adobe R Photoshop R and four quality areas were serially removed. In the objective method, each latent fingerprint was assessed with NFIQ algorithm and then MINDTCT algorithm. The MINDTCT algorithm provided a quality map that was used to remove successive portions of each latent fingerprint. The resulting new images from both methods were compared to a database using the two different minutiae-based matching algorithms: AFIX Tracker R and BOZORTH3. The results were examined utilizing the statistical methods of receiver operator characteristic (ROC) curves, area under the ROC curve (AUC), cumulative match characteristic (CMC) curve, Wilcoxon signed-rank test, Spearman's rank correlation and the comparison of the removal methods. ROC curves and the resulting AUC were able to determine that the AFIX Tracker R program is a reliable performer with high AUC values, while the BOZORTH3 minutiae-based algorithm did not perform well with low AUC scores of around 0.5. The results produced from the CMC curves showed that the subjective method produced higher rank 1 and top 10 rank identification than the objective method, contrary to what was hypothesized. The correlation scores showed the manual and automatic extraction were weakly correlated to one another. However, a very weak to no correlation between the algorithms of the BOZORTH3 and AFIX Tracker R was observed. The comparison between the subjective and objective methods of removal showed the examiner allowed for a more conservative removal of the fingerprint than the objective method. With this result in connection with the CMC curve results shows that being more conservative produces higher rank 1 and top ten rank identification scores.

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