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John M. Libert, Shahram Orandi, John D. Grantham, Michael D. Garris
This study examines the use of the NIST Spectral Image Validation and Verification (SIVV) metric for the application of detecting the sample rate of a given fingerprint digital image. SIVV operates by reducing an input image to a 1-dimensional power
Shahram Orandi, John M. Libert, John D. Grantham, Kenneth Ko, Stephen S. Wood, Frederick R. Byers, Bruce Bandini, Stephen G. Harvey, Michael D. Garris
This special publication provides guidance for compression of 1000 ppi friction ridge imagery as well as an interoperability pathway between legacy 500 ppi friction ridge imagery and new 1000 ppi friction ridge image data.
Fingerprint quality assessment is a crucial task which needs to be conducted accurately in various phases in the biometric enrolment and recognition processes. Neglecting quality measurement will adversely impact accuracy and efficiency of biometric
P J. Phillips, Alice O'Toole, Vaidehi Natu, Xiaobo An, Rice Allyson, James Ryland
The neural organization of person processing relies on brain regions functionally selective for faces or bodies, with a subset of these regions preferring moving stimuli. Although the response properties of the individual areas are well established, less
George Quinn, Patrick Grother, Mei Ngan, Nick Rymer
The IREX IV evaluation builds upon IREX III as a performance test of one-to-many iris recognition. This report is the second part of the IREX IV evaluation, which specifically, evaluates the ability of automated iris recognition algorithms to match heavily
Since 2005, human and machine performance has been systematically compared as part of face recognition competitions, with results being been reported for both still and video imagery. The key results from these competitions are reviewed. To analysis
Information useful for identifying a person can be found both in the face and body. Previous studies indicate that when an entire person is visible, we rely strongly on the face for identification, even if the body can be useful. In this study, we measured
P J. Phillips, J. R. Beveridge, Geof H. Givens, Bruce A. Draper, Yui M. Lui, David Bolme
The field of biometric face recognition blends methods from computer science, engineering and statistics, however statistical reasoning has been applied predominantly in the design of recognition algorithms. Here we broadly review face recognition from a
Yooyoung Lee, James J. Filliben, Ross J. Micheals, Michael D. Garris, P J. Phillips
We examine the robustness of algorithm performance over multiple datasets collected with different sensors. This study provide insight as to whether an algorithm performance derived from traditional controlled environment studies will robustly extrapolate
P J. Phillips, Rice Allyson, Vaidehi Natu, Xiabo An, Alice O'Toole
How do we recognize someone when the face fails? We show that people rely on the body, but are unaware of this. State-of-the-art face recognition algorithms were used to select images of people with no useful identity information in the face. Human
Shahram Orandi, John M. Libert, John D. Grantham, Frederick R. Byers, Lindsay M. Petersen, Michael D. Garris
This paper presents the findings of a study conducted to measure the impact of JPEG 2000 lossy compression on the comparison of 1000 ppi latent fingerprint imagery and 1000 ppi exemplar fingerprint imagery. Combinations of image pairs that vary by the