2012
DOI: 10.1117/12.922772
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Surface classification and detection of latent fingerprints based on 3D surface texture parameters

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Cited by 3 publications
(3 citation statements)
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“…Ssk shows the skewness of surface height distribution and represents the degree of symmetry of the surface heights about the mean plane (Gruhn et al. ), and it is useful in monitoring for different types of wear conditions (Whitehouse ). If Ssk is greater than zero, it indicates the predominance of peaks; if Ssk is less than zero, it means that valley structures have more quantity (Tudose et al.…”
Section: Discussionmentioning
confidence: 99%
“…Ssk shows the skewness of surface height distribution and represents the degree of symmetry of the surface heights about the mean plane (Gruhn et al. ), and it is useful in monitoring for different types of wear conditions (Whitehouse ). If Ssk is greater than zero, it indicates the predominance of peaks; if Ssk is less than zero, it means that valley structures have more quantity (Tudose et al.…”
Section: Discussionmentioning
confidence: 99%
“…Statistical Features: For this study we have selected eight out of 40 statistical features we identified in prior work [3]. These features are derived from industrial standards such as ASME B46.1 / ISO 4287/1 / ANSI B.46.1, see [1,4].…”
Section: Features For Fingerprint Detectionmentioning
confidence: 99%
“…This paper presents an approach, partly based on biometric methods, for a semi automated system for acquisition, detection, and localization of latent fingerprints using a chromatic white light (CWL) sensor, which is typically used in material science and industry. Based on previous work [2,3,5] new latent fingerprint detection features will be evaluated. Furthermore, certain fusion strategies, which are adopted from biometrics [8] are applied.…”
Section: Introductionmentioning
confidence: 99%