NDLI: Investigating the feasibility of image-based nose biometrics

Content Provider	IEEE Xplore Digital Library
Author	Zehngut, N. Juefei-Xu, F. Bardia, R. Pal, D.K. Bhagavatula, C. Savvides, M.
Copyright Year	2015
Description	Author affiliation: Carnegie Mellon Univ., Pittsburgh, PA, USA (Zehngut, N.; Juefei-Xu, F.; Bardia, R.; Pal, D.K.; Bhagavatula, C.; Savvides, M.)
Abstract	The search for new biometrics is never ending. In this work, we investigate the use of image based nasal features as a biometric. In many real-world recognition scenarios, partial occlusions on the face leave the nose region visible (e.g. sunglasses). Face recognition systems often fail or perform poorly in such settings. Furthermore, the nose region naturally contain more invariance to expression than features extracted from other parts of the face. In this study, we extract discriminative nasal features using Kernel Class-Dependence Feature Analysis (KCFA) based on Optimal Trade-off Synthetic Discriminant Function (OTSDF) filters. We evaluate this technique on the FRGC ver2.0 database and the AR Face database, training and testing exclusively on nasal features and have compared the results to the full face recognition using KCFA features. We find that the between-subject discriminability in nasal features is comparable to that found in facial features. This shows that nose biometrics have a potential to support and boost biometric identification, that has largely been under utilized. Moreover, our extracted KCFA nose features have significantly outperformed the PittPatt face matcher which works with the original JPEG images on the AR facial occlusion database. This shows that nose biometrics can be used as a stand-alone biometric trait when the subjects are under occlusions.
Starting Page	522
Ending Page	526
File Size	2887011
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7350853
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face Nose Biometrics (access control) Databases Training Correlation Feature extraction Nose Biometrics
Content Type	Text
Resource Type	Article

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