3D face recognition

I. Kakadiaris; G. Passalis; G. Toderici; N. Murtuza; T. Theoharis

3D face recognition

I. Kakadiaris, G. Passalis, G. Toderici, N. Murtuza, T. Theoharis

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper, we present a new 3D face recognition approach. Full automation is provided through the use of advanced multi-stage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact wavelet metadata. We present results on the largest known, and now publicly-available, Face Recognition Grand Challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, our approach has achieved the highest accuracy on this dataset.

Original language	English (US)
Pages	869-878
Number of pages	10
State	Published - 2006
Event	2006 17th British Machine Vision Conference, BMVC 2006 - Edinburgh, United Kingdom Duration: Sep 4 2006 → Sep 7 2006

Conference

Conference	2006 17th British Machine Vision Conference, BMVC 2006
Country/Territory	United Kingdom
City	Edinburgh
Period	9/4/06 → 9/7/06

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Cite this

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title = "3D face recognition",

abstract = "In this paper, we present a new 3D face recognition approach. Full automation is provided through the use of advanced multi-stage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact wavelet metadata. We present results on the largest known, and now publicly-available, Face Recognition Grand Challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, our approach has achieved the highest accuracy on this dataset.",

author = "I. Kakadiaris and G. Passalis and G. Toderici and N. Murtuza and T. Theoharis",

year = "2006",

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note = "2006 17th British Machine Vision Conference, BMVC 2006 ; Conference date: 04-09-2006 Through 07-09-2006",

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AU - Kakadiaris, I.

AU - Passalis, G.

AU - Toderici, G.

AU - Murtuza, N.

AU - Theoharis, T.

PY - 2006

Y1 - 2006

N2 - In this paper, we present a new 3D face recognition approach. Full automation is provided through the use of advanced multi-stage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact wavelet metadata. We present results on the largest known, and now publicly-available, Face Recognition Grand Challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, our approach has achieved the highest accuracy on this dataset.

AB - In this paper, we present a new 3D face recognition approach. Full automation is provided through the use of advanced multi-stage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact wavelet metadata. We present results on the largest known, and now publicly-available, Face Recognition Grand Challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, our approach has achieved the highest accuracy on this dataset.

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M3 - Paper

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T2 - 2006 17th British Machine Vision Conference, BMVC 2006

Y2 - 4 September 2006 through 7 September 2006

ER -

3D face recognition

Abstract

Conference

ASJC Scopus subject areas

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