3D face recognition using topographic high-order derivatives

Ali Cheraghian; Farshid Hajati; Ajmal S Mian; Yongsheng Gao; Soheila Gheisari

doi:10.1109/icip.2013.6738764

3D face recognition using topographic high-order derivatives

Ali Cheraghian
, Farshid Hajati
, Ajmal S Mian
, Yongsheng Gao
, Soheila Gheisari

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 highorder 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	2013 IEEE International Conference on Image Processing
Place of Publication	United States of America
Publisher	Institute of Electrical and Electronics Engineers
Pages	3705-3709
ISBN (Print)	9781479923410
DOIs	https://doi.org/10.1109/icip.2013.6738764
Publication status	Published - 13 Feb 2014
Event	2013 IEEE International Conference on Image Processing - Melbourne Convention and Exhibition Centre (MCEC), Melbourne, Australia Duration: 15 Sept 2013 → 18 Sept 2013

Conference

Conference	2013 IEEE International Conference on Image Processing
City	Melbourne, Australia
Period	15/09/13 → 18/09/13

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10.1109/icip.2013.6738764

https://hdl.handle.net/1959.11/70765

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@inproceedings{06f0769163614e90ad2bc273002d1483,

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 highorder 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 = "Ali Cheraghian and Farshid Hajati and Mian, \{Ajmal S\} and Yongsheng Gao and Soheila Gheisari",

year = "2014",

month = feb,

day = "13",

doi = "10.1109/icip.2013.6738764",

language = "English",

isbn = "9781479923410",

pages = "3705--3709",

booktitle = "2013 IEEE International Conference on Image Processing",

publisher = "Institute of Electrical and Electronics Engineers",

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

}

Cheraghian, A, Hajati, F, Mian, AS, Gao, Y & Gheisari, S 2014, 3D face recognition using topographic high-order derivatives. in 2013 IEEE International Conference on Image Processing. Institute of Electrical and Electronics Engineers, United States of America, pp. 3705-3709, 2013 IEEE International Conference on Image Processing, Melbourne, 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, Ali

AU - Hajati, Farshid

AU - Mian, Ajmal S

AU - Gao, Yongsheng

AU - Gheisari, Soheila

PY - 2014/2/13

Y1 - 2014/2/13

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 highorder 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 highorder 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 contribution

SN - 9781479923410

SP - 3705

EP - 3709

BT - 2013 IEEE International Conference on Image Processing

PB - Institute of Electrical and Electronics Engineers

CY - United States of America

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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