3D Face Recognition: Feature Extraction based on Directional Signatures From Range Data and Disparity Maps

Soodamani Ramalingam

doi:10.1109/SMC.2013.750

3D Face Recognition: Feature Extraction based on Directional Signatures From Range Data and Disparity Maps

Soodamani Ramalingam

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

4 Citations (Scopus)

Abstract

In this paper, the author presents a work on i) range data and ii) stereo-vision system based disparity map profiling that are used as signatures for 3D face recognition. The signatures capture the intensity variations along a line at sample points on a face in any particular direction. The directional signatures and some of their combinations are compared to study the variability in recognition performances. Two 3D face image datasets namely, a local student database captured with a stereo vision system and the FRGC v1 range dataset are used for performance evaluation.

Original language	English
Title of host publication	Procs IEEE Int Conf on Systems, Man and Cybernetics 2013
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	4397-4402
ISBN (Print)	978-0-7695-5154-8
DOIs	https://doi.org/10.1109/SMC.2013.750
Publication status	Published - 14 Oct 2013
Event	IEEE International Conference on Systems Man and Cybernetics, IEEE SMC 2013 - Manchester, United Kingdom Duration: 13 Oct 2013 → 16 Oct 2013

Conference

Conference	IEEE International Conference on Systems Man and Cybernetics, IEEE SMC 2013
Country/Territory	United Kingdom
City	Manchester
Period	13/10/13 → 16/10/13

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10.1109/SMC.2013.750

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@inproceedings{a8b8fb14cb3d446c84e19d69085d399b,

title = "3D Face Recognition: Feature Extraction based on Directional Signatures From Range Data and Disparity Maps",

abstract = "In this paper, the author presents a work on i) range data and ii) stereo-vision system based disparity map profiling that are used as signatures for 3D face recognition. The signatures capture the intensity variations along a line at sample points on a face in any particular direction. The directional signatures and some of their combinations are compared to study the variability in recognition performances. Two 3D face image datasets namely, a local student database captured with a stereo vision system and the FRGC v1 range dataset are used for performance evaluation.",

author = "Soodamani Ramalingam",

year = "2013",

month = oct,

day = "14",

doi = "10.1109/SMC.2013.750",

language = "English",

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pages = "4397--4402",

booktitle = "Procs IEEE Int Conf on Systems, Man and Cybernetics 2013",

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

address = "United States",

note = "IEEE International Conference on Systems Man and Cybernetics, IEEE SMC 2013 ; Conference date: 13-10-2013 Through 16-10-2013",

}

Ramalingam, S 2013, 3D Face Recognition: Feature Extraction based on Directional Signatures From Range Data and Disparity Maps. in Procs IEEE Int Conf on Systems, Man and Cybernetics 2013. Institute of Electrical and Electronics Engineers (IEEE), pp. 4397-4402, IEEE International Conference on Systems Man and Cybernetics, IEEE SMC 2013, Manchester, United Kingdom, 13/10/13. https://doi.org/10.1109/SMC.2013.750

TY - GEN

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AU - Ramalingam, Soodamani

PY - 2013/10/14

Y1 - 2013/10/14

N2 - In this paper, the author presents a work on i) range data and ii) stereo-vision system based disparity map profiling that are used as signatures for 3D face recognition. The signatures capture the intensity variations along a line at sample points on a face in any particular direction. The directional signatures and some of their combinations are compared to study the variability in recognition performances. Two 3D face image datasets namely, a local student database captured with a stereo vision system and the FRGC v1 range dataset are used for performance evaluation.

AB - In this paper, the author presents a work on i) range data and ii) stereo-vision system based disparity map profiling that are used as signatures for 3D face recognition. The signatures capture the intensity variations along a line at sample points on a face in any particular direction. The directional signatures and some of their combinations are compared to study the variability in recognition performances. Two 3D face image datasets namely, a local student database captured with a stereo vision system and the FRGC v1 range dataset are used for performance evaluation.

U2 - 10.1109/SMC.2013.750

DO - 10.1109/SMC.2013.750

M3 - Conference contribution

SN - 978-0-7695-5154-8

SP - 4397

EP - 4402

BT - Procs IEEE Int Conf on Systems, Man and Cybernetics 2013

PB - Institute of Electrical and Electronics Engineers (IEEE)

Y2 - 13 October 2013 through 16 October 2013

ER -

3D Face Recognition: Feature Extraction based on Directional Signatures From Range Data and Disparity Maps

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