Unified 3D face and ear recognition using wavelets on geometry images

Theoharis Theoharis; Georgios Passalis; George Toderici; Ioannis A. Kakadiaris

doi:10.1016/j.patcog.2007.06.024

Unified 3D face and ear recognition using wavelets on geometry images

Theoharis Theoharis, Georgios Passalis, George Toderici, Ioannis A. Kakadiaris

Research output: Contribution to journal › Article › peer-review

62 Scopus citations

Abstract

As the accuracy of biometrics improves, it is getting increasingly hard to push the limits using a single modality. In this paper, a unified approach that fuses three-dimensional facial and ear data is presented. An annotated deformable model is fitted to the data and a geometry image is extracted. Wavelet coefficients are computed from the geometry image and used as a biometric signature. The method is evaluated using the largest publicly available database and achieves 99.7% rank-one recognition rate. The state-of-the-art accuracy of the multimodal fusion is attributed to the low correlation between the individual differentiability of the two modalities.

Original language	English (US)
Pages (from-to)	796-804
Number of pages	9
Journal	Pattern Recognition
Volume	41
Issue number	3
DOIs	https://doi.org/10.1016/j.patcog.2007.06.024
State	Published - Mar 2008

Keywords

Deformable models
Ear recognition
Face recognition
Geometry images
Multimodal biometrics
Wavelets

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/j.patcog.2007.06.024

Cite this

@article{271b0dcd1b8e491e9670935382fccfdc,

title = "Unified 3D face and ear recognition using wavelets on geometry images",

abstract = "As the accuracy of biometrics improves, it is getting increasingly hard to push the limits using a single modality. In this paper, a unified approach that fuses three-dimensional facial and ear data is presented. An annotated deformable model is fitted to the data and a geometry image is extracted. Wavelet coefficients are computed from the geometry image and used as a biometric signature. The method is evaluated using the largest publicly available database and achieves 99.7\% rank-one recognition rate. The state-of-the-art accuracy of the multimodal fusion is attributed to the low correlation between the individual differentiability of the two modalities.",

keywords = "Deformable models, Ear recognition, Face recognition, Geometry images, Multimodal biometrics, Wavelets",

author = "Theoharis Theoharis and Georgios Passalis and George Toderici and Kakadiaris, \{Ioannis A.\}",

note = "Funding Information: About the Author —IOANNIS A. KAKADIARIS received the Ptychion (B.Sc.) in Physics from the University of Athens, Greece, in 1989, the M.Sc. in Computer Science from Northeastern University, Boston, MA, in 2001, and the Ph.D. in Computer Science from University of Pennsylvania, Philadelphia, PA, in 2007. Dr. Kakadiaris joined the University of Houston (UH) in August 1997 after completing a Post-Doctoral Fellowship at the University of Pennsylvania. He is the founder and Director of UHs Computational Biomedicine Laboratory (formerly the Visual Computing Lab) and Director of the Division of Bio-Imaging and Bio-Computation at the UH Institute for Digital Informatics and Analysis. Dr. Kakadiaris{\textquoteright} research interests include biomedical image analysis, computational biomedicine, biometrics, computer vision, and pattern recognition. Dr. Kakadiaris is the recipient of the year 2000 NSF Early Career Development Award, UH Computer Science Research Excellence Award, UH Enron Teaching Excellence Award, James Muller VP Young Investigator Prize, and the Schlumberger Technical Foundation Award. Funding Information: Partial financial support from the Hellenic General Secretariat of Research and Technology under Project 05NON-EU-91 is acknowledged. ",

year = "2008",

month = mar,

doi = "10.1016/j.patcog.2007.06.024",

language = "English (US)",

volume = "41",

pages = "796--804",

journal = "Pattern Recognition",

issn = "0031-3203",

publisher = "Elsevier Ltd",

number = "3",

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AU - Theoharis, Theoharis

AU - Passalis, Georgios

AU - Toderici, George

AU - Kakadiaris, Ioannis A.

N1 - Funding Information: About the Author —IOANNIS A. KAKADIARIS received the Ptychion (B.Sc.) in Physics from the University of Athens, Greece, in 1989, the M.Sc. in Computer Science from Northeastern University, Boston, MA, in 2001, and the Ph.D. in Computer Science from University of Pennsylvania, Philadelphia, PA, in 2007. Dr. Kakadiaris joined the University of Houston (UH) in August 1997 after completing a Post-Doctoral Fellowship at the University of Pennsylvania. He is the founder and Director of UHs Computational Biomedicine Laboratory (formerly the Visual Computing Lab) and Director of the Division of Bio-Imaging and Bio-Computation at the UH Institute for Digital Informatics and Analysis. Dr. Kakadiaris’ research interests include biomedical image analysis, computational biomedicine, biometrics, computer vision, and pattern recognition. Dr. Kakadiaris is the recipient of the year 2000 NSF Early Career Development Award, UH Computer Science Research Excellence Award, UH Enron Teaching Excellence Award, James Muller VP Young Investigator Prize, and the Schlumberger Technical Foundation Award. Funding Information: Partial financial support from the Hellenic General Secretariat of Research and Technology under Project 05NON-EU-91 is acknowledged.

PY - 2008/3

Y1 - 2008/3

N2 - As the accuracy of biometrics improves, it is getting increasingly hard to push the limits using a single modality. In this paper, a unified approach that fuses three-dimensional facial and ear data is presented. An annotated deformable model is fitted to the data and a geometry image is extracted. Wavelet coefficients are computed from the geometry image and used as a biometric signature. The method is evaluated using the largest publicly available database and achieves 99.7% rank-one recognition rate. The state-of-the-art accuracy of the multimodal fusion is attributed to the low correlation between the individual differentiability of the two modalities.

AB - As the accuracy of biometrics improves, it is getting increasingly hard to push the limits using a single modality. In this paper, a unified approach that fuses three-dimensional facial and ear data is presented. An annotated deformable model is fitted to the data and a geometry image is extracted. Wavelet coefficients are computed from the geometry image and used as a biometric signature. The method is evaluated using the largest publicly available database and achieves 99.7% rank-one recognition rate. The state-of-the-art accuracy of the multimodal fusion is attributed to the low correlation between the individual differentiability of the two modalities.

KW - Deformable models

KW - Ear recognition

KW - Face recognition

KW - Geometry images

KW - Multimodal biometrics

KW - Wavelets

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Unified 3D face and ear recognition using wavelets on geometry images

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