3D face recognition using topographic high-order derivatives

A. Cheraghian; F. Hajati; Ajmal Mian; G. Yongsheng; S. Gheisari

doi:10.1109/ICIP.2013.6738764

3D face recognition using topographic high-order derivatives

A. Cheraghian, F. Hajati, Ajmal Mian, G. Yongsheng, S. Gheisari

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Abstract

This paper presents a novel feature, Topographic High-order Derivatives (THD) for 3D face recognition. THD is based on the high-order micro-pattern information extracted from face topography maps. Face topography maps are partitioned into polar sectors, and THDs are computed using directional high-order derivatives within the sectors. Local features are extracted by encoding directional high-order derivatives within polar neighborhoods. To evaluate the proposed method, we use Bosphorus and FRGC 3D face databases which include pose and expression changes. The performance of the proposed method is higher compared to the state-of-the-art benchmark approaches in 3D face recognition.

Original language	English
Title of host publication	Proceedings of 20th IEEE International Conference on Image Processing (ICIP), 2013
Place of Publication	Australia
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	3705-3709
ISBN (Print)	9781479923410
DOIs	https://doi.org/10.1109/ICIP.2013.6738764
Publication status	Published - 2013
Event	2013 IEEE International Conference on Image Processing - Melbourne, Australia, Melbourne, Australia Duration: 15 Sept 2013 → 18 Sept 2013 Conference number: 20th

Conference

Conference	2013 IEEE International Conference on Image Processing
Abbreviated title	ICIP 2013
Country/Territory	Australia
City	Melbourne
Period	15/09/13 → 18/09/13

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title = "3D face recognition using topographic high-order derivatives",

abstract = "This paper presents a novel feature, Topographic High-order Derivatives (THD) for 3D face recognition. THD is based on the high-order micro-pattern information extracted from face topography maps. Face topography maps are partitioned into polar sectors, and THDs are computed using directional high-order derivatives within the sectors. Local features are extracted by encoding directional high-order derivatives within polar neighborhoods. To evaluate the proposed method, we use Bosphorus and FRGC 3D face databases which include pose and expression changes. The performance of the proposed method is higher compared to the state-of-the-art benchmark approaches in 3D face recognition.",

author = "A. Cheraghian and F. Hajati and Ajmal Mian and G. Yongsheng and S. Gheisari",

year = "2013",

doi = "10.1109/ICIP.2013.6738764",

language = "English",

isbn = "9781479923410",

pages = "3705--3709",

booktitle = "Proceedings of 20th IEEE International Conference on Image Processing (ICIP), 2013",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2013 IEEE International Conference on Image Processing, ICIP 2013 ; Conference date: 15-09-2013 Through 18-09-2013",

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Cheraghian, A, Hajati, F, Mian, A, Yongsheng, G & Gheisari, S 2013, 3D face recognition using topographic high-order derivatives. in Proceedings of 20th IEEE International Conference on Image Processing (ICIP), 2013. IEEE, Institute of Electrical and Electronics Engineers, Australia, pp. 3705-3709, 2013 IEEE International Conference on Image Processing, Melbourne, Victoria, Australia, 15/09/13. https://doi.org/10.1109/ICIP.2013.6738764

TY - GEN

T1 - 3D face recognition using topographic high-order derivatives

AU - Cheraghian, A.

AU - Hajati, F.

AU - Mian, Ajmal

AU - Yongsheng, G.

AU - Gheisari, S.

N1 - Conference code: 20th

PY - 2013

Y1 - 2013

N2 - This paper presents a novel feature, Topographic High-order Derivatives (THD) for 3D face recognition. THD is based on the high-order micro-pattern information extracted from face topography maps. Face topography maps are partitioned into polar sectors, and THDs are computed using directional high-order derivatives within the sectors. Local features are extracted by encoding directional high-order derivatives within polar neighborhoods. To evaluate the proposed method, we use Bosphorus and FRGC 3D face databases which include pose and expression changes. The performance of the proposed method is higher compared to the state-of-the-art benchmark approaches in 3D face recognition.

AB - This paper presents a novel feature, Topographic High-order Derivatives (THD) for 3D face recognition. THD is based on the high-order micro-pattern information extracted from face topography maps. Face topography maps are partitioned into polar sectors, and THDs are computed using directional high-order derivatives within the sectors. Local features are extracted by encoding directional high-order derivatives within polar neighborhoods. To evaluate the proposed method, we use Bosphorus and FRGC 3D face databases which include pose and expression changes. The performance of the proposed method is higher compared to the state-of-the-art benchmark approaches in 3D face recognition.

U2 - 10.1109/ICIP.2013.6738764

DO - 10.1109/ICIP.2013.6738764

M3 - Conference paper

SN - 9781479923410

SP - 3705

EP - 3709

BT - Proceedings of 20th IEEE International Conference on Image Processing (ICIP), 2013

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Australia

T2 - 2013 IEEE International Conference on Image Processing

Y2 - 15 September 2013 through 18 September 2013

ER -

3D face recognition using topographic high-order derivatives

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