research-article

3D face landmark labelling

Authors:

Clement Creusot,

Jim AustinAuthors Info & Claims

3DOR '10: Proceedings of the ACM workshop on 3D object retrieval

Pages 27 - 32

https://doi.org/10.1145/1877808.1877815

Published: 25 October 2010 Publication History

Abstract

Most 3D face processing systems require feature detection and localisation, for example to crop, register, analyse or recognise faces. The three features often used in the literature are the tip of the nose, and the two inner corner of the eyes. Failure to localise these landmarks can cause the system to fail and they become very difficult to detect under large pose variations or when occlusion is present. In this paper, we present a proof-of-concept for a face labelling system capable of overcoming this problem, as a larger number of landmarks are employed. A set of points containing hand-placed landmarks is used as input data. The aim here is to retrieve the landmark's labels when some part of the face is missing. By using graph matching techniques to reduce the number of candidates, and translation and unit-quaternion clustering to determine a final correspondence, we evaluate the accuracy at which landmarks can be retrieved under changes in expression, orientation and in the presence of occlusions.

References

[1]

P. Besl and N. McKay. A method for registration of 3d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):239--256, 1992.

Digital Library

[2]

K. I. Chang, K. Bowyer, and P. Flynn. Multiple nose region matching for 3d face recognition under varying facial expression. Pattern Analysis and Machine Intelligence, 28(10):1695--1700, Oct. 2006.

Digital Library

[3]

W. J. Christmas, J. Kittler, and M. Petrou. Structural matching in computer vision using probabilistic relaxation. IEEE Trans. Pattern Anal. Mach. Intell., 17(8):749--764, 1995.

Digital Library

[4]

D. Colbry, X. Lu, A. K. Jain, and G. Stockman. 3d face feature extraction for recognition. Technical Report MSU-CSE-04--39, Department of Computer Science, Michigan State University, September 2004.

[5]

A. Colombo, C. Cusano, and R. Schettini. Gappy pca classification for occlusion tolerant 3d face detection. J. Math. Imaging Vis., 35(3):193--207, 2009.

Digital Library

[6]

J. D'Hose, J. Colineau, C. Bichon, and B. Dorizzi. Precise localization of landmarks on 3d faces using gabor wavelets. In BTAS 2007, pages 1--6, Sept. 2007.

[7]

H. Dibeklioglu, A. Salah, and L. Akarun. 3d facial landmarking under expression, pose, and occlusion variations. In BTAS08, pages 1--6, 2008.

[8]

T. Faltemier, K. Bowyer, and P. Flynn. Rotated profile signatures for robust 3d feature detection. In Automatic Face Gesture Recognition, pages 1--7, 2008.

[9]

C. Gramkow. On averaging rotations. International Journal of Computer Vision, 42(1):7--16, Apr. 2001.

Digital Library

[10]

S. Gupta, M. K. Markey, J. Aggarwal, and A. C. Bovik. Three dimensional face recognition based on geodesic and euclidean distances. In IS&T/SPIE Symposium on Electronic Imaging: Vision Geometry XV, 2007.

[11]

B. Horn. Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America A, 4(4):629--642, 1987.

[12]

A. Johnson. Spin-Images: A Representation for 3-D Surface Matching. PhD thesis, Robotics Institute, Carnegie Mellon University, August 1997.

[13]

J. J. Koenderink and A. J. van Doorn. Surface shape and curvature scales. Image Vision Comput., 10(8):557--565, 1992.

Digital Library

[14]

X. Lu and A. Jain. Automatic feature extraction for multiview 3d face recognition. In Automatic Face and Gesture Recognition, pages 585--590, 2--6 2006.

Digital Library

[15]

X. Lu, A. Jain, and D. Colbry. Matching 2.5d face scans to 3d models. Pattern Analysis and Machine Intelligence, 28(1):31--43, Jan. 2006.

Digital Library

[16]

A. S. Mian, M. Bennamoun, and R. A. Owens. Automatic 3d face detection, normalization and recognition. In 3DPVT, pages 735--742, 2006.

Digital Library

[17]

N. Pears, T. Heseltine, and M. Romero. From 3d point clouds to pose-normalised depth maps. International Journal of Computer Vision, 89(2):152--176, Sept. 2010.

Digital Library

[18]

P. Phillips, P. Flynn, T. Scruggs, K. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek. Overview of the face recognition grand challenge. Computer Vision and Pattern Recognition, 2005, 1:947--954, June 2005.

Digital Library

[19]

M. Romero and N. Pears. Landmark localisation in 3d face data. In Advanced Video and Signal Based Surveillance, 2009., pages 73--78, Sept. 2009. u g ¸

Digital Library

[20]

A. Savran, N. Alyuz, H. Dibeklioglu, O. Celiktutan, B. Gokberk, B. Sankur, and L. Akarun. Bosphorus o database for 3d face analysis. In Biometrics and Identity Management, 2008, pages 47--56, 2008.

Digital Library

[21]

M. Segundo, C. Queirolo, O. Bellon, and L. Silva. Automatic 3D facial segmentation and landmark detection. Image Analysis and Processing, 2007, pages 431--436, Sept. 2007.

Digital Library

[22]

P. Szeptycki, M. Ardabilian, and L. Chen. A coarse-to-fine curvature analysis-based rotation invariant 3D face landmarking. In BTAS, pages 32--37, Sept. 2009.

Digital Library

[23]

M. Turner and J. Austin. Graph matching by neural relaxation. Neural Computing & Applications, 7(3):238--248, 1998.

Cited By

Topsakal OAkinci TMurphy JPreston TCelikoyar M(2023)Detecting Facial Landmarks on 3D Models Based on Geometric Properties—A Review of Algorithms, Enhancements, Additions and Open-Source ImplementationsIEEE Access10.1109/ACCESS.2023.325509911(25593-25603)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3255099
Bagchi PBhattacharjee DNasipuri M(2022)Registration of Point Clouds for Human Face RecognitionIntelligent Technologies: Concepts, Applications, and Future Directions10.1007/978-981-19-1021-0_2(29-56)Online publication date: 22-May-2022
https://doi.org/10.1007/978-981-19-1021-0_2
Agbolade ONazri AYaakob RGhani ACheah Y(2020)Landmark-based homologous multi-point warping approach to 3D facial recognition using multiple datasetsPeerJ Computer Science10.7717/peerj-cs.2496(e249)Online publication date: 16-Jan-2020
https://doi.org/10.7717/peerj-cs.249
Show More Cited By

Index Terms

3D face landmark labelling
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding

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Published In

cover image ACM Conferences

3DOR '10: Proceedings of the ACM workshop on 3D object retrieval

October 2010

96 pages

ISBN:9781450301602

DOI:10.1145/1877808

Program Chairs:
Mohamed Doudi
TELECOM Lille1/LIFL, France
,
Michela Spagnuolo
CNR-IMATI, Italy
,
Remco Veltkamp
Utrecht University, The Netherlands

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 October 2010

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October 25, 2010

Firenze, Italy

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Cited By

Topsakal OAkinci TMurphy JPreston TCelikoyar M(2023)Detecting Facial Landmarks on 3D Models Based on Geometric Properties—A Review of Algorithms, Enhancements, Additions and Open-Source ImplementationsIEEE Access10.1109/ACCESS.2023.325509911(25593-25603)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3255099
Bagchi PBhattacharjee DNasipuri M(2022)Registration of Point Clouds for Human Face RecognitionIntelligent Technologies: Concepts, Applications, and Future Directions10.1007/978-981-19-1021-0_2(29-56)Online publication date: 22-May-2022
https://doi.org/10.1007/978-981-19-1021-0_2
Agbolade ONazri AYaakob RGhani ACheah Y(2020)Landmark-based homologous multi-point warping approach to 3D facial recognition using multiple datasetsPeerJ Computer Science10.7717/peerj-cs.2496(e249)Online publication date: 16-Jan-2020
https://doi.org/10.7717/peerj-cs.249
Nazri AAgbolade OYaakob RGhani ACheah Y(2020)A novel investigation of the effect of iterations in sliding semi-landmarks for 3D human facial imagesBMC Bioinformatics10.1186/s12859-020-3497-721:1Online publication date: 24-May-2020
https://doi.org/10.1186/s12859-020-3497-7
Manal EArsalane ZAicha M(2020)Automated Detection of Craniofacial Landmarks on a 3D Facial MeshAdvances in Integrated Design and Production10.1007/978-3-030-62199-5_47(537-548)Online publication date: 27-Nov-2020
https://doi.org/10.1007/978-3-030-62199-5_47
Pears NMian A(2020)3D Face Recognition3D Imaging, Analysis and Applications10.1007/978-3-030-44070-1_12(569-630)Online publication date: 12-Sep-2020
https://doi.org/10.1007/978-3-030-44070-1_12
Agbolade ONazri AYaakob RGhani ACheah Y(2019)3-Dimensional facial expression recognition in human using multi-points warpingBMC Bioinformatics10.1186/s12859-019-3153-220:1Online publication date: 2-Dec-2019
https://doi.org/10.1186/s12859-019-3153-2
Agbolade ONazri AYaakob RGhani ACheah Y(2019)3D Homologous Multi-Points Warping Application to Sexual Dimorphism in Human Face2019 3rd International Conference on Imaging, Signal Processing and Communication (ICISPC)10.1109/ICISPC.2019.8935694(166-171)Online publication date: Jul-2019
https://doi.org/10.1109/ICISPC.2019.8935694
Agbolade ONazri AYaakob RGhani AKqueen Cheah Y(2019)Homologous Multi-Points Warping: An Algorithm for Automatic 3D Facial Landmark2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)10.1109/I2CACIS.2019.8825072(79-84)Online publication date: Jun-2019
https://doi.org/10.1109/I2CACIS.2019.8825072
Agbolade ONazri AYaakob RGhani ACheah Y(2019)Landmark-based Multi-Points Warping Approach to 3D Facial Expression Recognition in Human2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)10.1109/AiDAS47888.2019.8970972(180-185)Online publication date: Sep-2019
https://doi.org/10.1109/AiDAS47888.2019.8970972
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