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Topic Area: Biometrics

Displaying records 51 to 60 of 288 records.
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51. Examination of Downsampling Strategies for Converting 1000 ppi Fingerprint Imagery to 500 ppi
Series: NIST Interagency/Internal Report (NISTIR)
Report Number: 7839
Topic: Biometrics
Published: 1/22/2013
Authors: Shahram Orandi, John M Libert, John Donald Grantham, Lepley Margaret, Bruce Bandini, Kenneth Ko, Lindsay M. Petersen, Stephen S Wood, Stephen G. Harvey
Abstract: Currently the bulk of fingerprint data in operational is captured, processed and stored at 500 ppi using the WSQ compressed digital format. With the transition to 1000 ppi, some systems will unavoidably contain an overlap between 500 ppi and 1000 ppi ...

52. Video-based Face Recognition via Joint Sparse Representation
Series: NIST Interagency/Internal Report (NISTIR)
Report Number: 7906
Topic: Biometrics
Published: 1/2/2013
Authors: P Jonathon Phillips, Vishal M Patel, Yi-Chen Chen, Rama Chellappa
Abstract: In video-based face recognition, a key challenge is in exploiting the extra information available in a video. In addition, different video sequences of the same subject may contain variations in resolution, illumination, pose, and facial expressions. ...

53. Advances in Biometric Standardisation ‹ Addressing Global Market Requirements for Biometric Standards
Topic: Biometrics
Published: 1/1/2013
Author: Fernando L Podio
Abstract: The paper discusses the current status of biometric standards development activities, with a focus on international standards developments. Published standards, as well as standards under development or planned for the near future, are addressed. T ...

54. Dictionary-based Face Recognition from Video
Topic: Biometrics
Published: 12/10/2012
Authors: P Jonathon Phillips, Yi-Chen Chen, Vishal M Patel, Rama Chellappa
Abstract: The main challenge in recognizing faces in video is effec- tively exploiting the multiple frames of a face and the accompanying dynamic signature. One prominent method is based on extracting joint appearance and behavioral features. A second method ...

55. Empirical Evidence for Increased False Reject Rate with Time Lapse in ICE 2006
Topic: Biometrics
Published: 12/4/2012
Authors: P Jonathon Phillips, Sarah E. Baker, Kevin W. Bowyer, Patrick J. Flynn

56. Biometrics in a Networked World
Topic: Biometrics
Published: 12/2/2012
Author: Kevin C Mangold

57. Preliminary Studies on the Good, the Bad, and the Ugly Face Recognition Challenge Problem
Topic: Biometrics
Published: 11/26/2012
Authors: P Jonathon Phillips, J. R. Beveridge, David Bolme, Bruce A. Draper, Yui M. Lui
Abstract: Face recognition has made significant advances over the last twenty years. State-of-the-art algorithms push the performanceenvelope to near perfect recognition rates on many face databases. Recently, the Good, the Bad, and the Ugly (GBU) face challen ...

58. Demographic Effects on Estimates of Automatic Face Recognition Performance
Topic: Biometrics
Published: 11/22/2012
Authors: P Jonathon Phillips, Alice J. O'Toole, Xiaobo An, Joseph Dunlop
Abstract: The intended applications of automatic face recognition systems include venues that vary widely in demographic diversity. Formal evaluations of algorithms do not commonly consider the effects of population diversity on performance. We document the ef ...

59. A Grassmann Manifold-based Domain Adaptation Approach
Topic: Biometrics
Published: 11/20/2012
Authors: P Jonathon Phillips, Jingjing Zheng, Ming-Yu Liu, Rama Chellappa
Abstract: Domain adaptation algorithms that handle shifts in the distribution between training and testing data are receiving much attention in computer vision. Recently, a Grassmann manifold-based domain adaptation algorithm that models the domain shift using ...

60. The Good, the Bad, and the Ugly Face Challenge Problem
Topic: Biometrics
Published: 11/20/2012
Authors: P Jonathon Phillips, J. R. Beveridge, Bruce A. Draper, G. H. Givens, Alice J. O'Toole, David Bolme, Joseph Dunlop, Yui M. Lui, Hassan A. Sahibzada, Samuel Weimer
Abstract: The Good, the Bad, & the Ugly Face Challenge Problem was created to encourage the development of algorithms that are robust to recognition across changes that occur in still frontal faces. The Good, the Bad, & the Ugly consists of three partitions. T ...

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