3D facial geometric features for constrained local model

Shiyang Cheng; Stefanos Zafeiriou; Ashish Asthana; Akshay Asthana; Maja Pantic

doi:10.1109/ICIP.2014.7025285

3D facial geometric features for constrained local model

Shiyang Cheng, Stefanos Zafeiriou, Ashish Asthana, Akshay Asthana, Maja Pantic

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

20 Citations (Scopus)

237 Downloads (Pure)

Abstract

We propose a 3D Constrained Local Model framework for deformable face alignment in depth image. Our framework exploits the intrinsic 3D geometric information in depth data by utilizing robust histogram-based 3D geometric features that are based on normal vectors. In addition, we demonstrate the fusion of intensity data and 3D features that further improves the facial landmark localization accuracy. The experiments are conducted on publicly available FRGC database. The results show that our 3D features based CLM completely outperforms the raw depth features based CLM in term of fitting accuracy and robustness, and the fusion of intensity and 3D depth feature further improves the performance. Another benefit is that the proposed 3D features in our framework do not require any pre-processing procedure on the data.

Original language	Undefined
Title of host publication	Proceedings of IEEE International Conference on Image Processing (ICIP 2014)
Place of Publication	USA
Publisher	IEEE
Pages	1425-1429
Number of pages	5
ISBN (Print)	978-1-4799-5751-4
DOIs	https://doi.org/10.1109/ICIP.2014.7025285
Publication status	Published - Oct 2014
Event	IEEE International Conference on Image Processing, ICIP 2014 - Paris, France Duration: 27 Oct 2014 → 30 Oct 2014 https://icip2014.wp.imt.fr/

Publication series

Name
Publisher	IEEE Computer Society

Conference

Conference	IEEE International Conference on Image Processing, ICIP 2014
Abbreviated title	ICIP
Country/Territory	France
City	Paris
Period	27/10/14 → 30/10/14
Internet address	https://icip2014.wp.imt.fr/

Keywords

EWI-25826
HMI-HF: Human Factors
deformable face alignment
IR-95232
Constrained Local Model
3D facial geometry
METIS-309951
histogram-based 3D feature

Access to Document

10.1109/ICIP.2014.7025285

Cite this

@inproceedings{a53174623b3e4087be1c39ed9d4665dc,

title = "3D facial geometric features for constrained local model",

abstract = "We propose a 3D Constrained Local Model framework for deformable face alignment in depth image. Our framework exploits the intrinsic 3D geometric information in depth data by utilizing robust histogram-based 3D geometric features that are based on normal vectors. In addition, we demonstrate the fusion of intensity data and 3D features that further improves the facial landmark localization accuracy. The experiments are conducted on publicly available FRGC database. The results show that our 3D features based CLM completely outperforms the raw depth features based CLM in term of fitting accuracy and robustness, and the fusion of intensity and 3D depth feature further improves the performance. Another benefit is that the proposed 3D features in our framework do not require any pre-processing procedure on the data.",

keywords = "EWI-25826, HMI-HF: Human Factors, deformable face alignment, IR-95232, Constrained Local Model, 3D facial geometry, METIS-309951, histogram-based 3D feature",

author = "Shiyang Cheng and Stefanos Zafeiriou and Ashish Asthana and Akshay Asthana and Maja Pantic",

note = "eemcs-eprint-25826 ; IEEE International Conference on Image Processing, ICIP 2014 ; Conference date: 27-10-2014 Through 30-10-2014",

year = "2014",

month = oct,

doi = "10.1109/ICIP.2014.7025285",

language = "Undefined",

isbn = "978-1-4799-5751-4",

publisher = "IEEE",

pages = "1425--1429",

booktitle = "Proceedings of IEEE International Conference on Image Processing (ICIP 2014)",

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}

TY - GEN

T1 - 3D facial geometric features for constrained local model

AU - Cheng, Shiyang

AU - Zafeiriou, Stefanos

AU - Asthana, Ashish

AU - Asthana, Akshay

AU - Pantic, Maja

N1 - eemcs-eprint-25826

PY - 2014/10

Y1 - 2014/10

N2 - We propose a 3D Constrained Local Model framework for deformable face alignment in depth image. Our framework exploits the intrinsic 3D geometric information in depth data by utilizing robust histogram-based 3D geometric features that are based on normal vectors. In addition, we demonstrate the fusion of intensity data and 3D features that further improves the facial landmark localization accuracy. The experiments are conducted on publicly available FRGC database. The results show that our 3D features based CLM completely outperforms the raw depth features based CLM in term of fitting accuracy and robustness, and the fusion of intensity and 3D depth feature further improves the performance. Another benefit is that the proposed 3D features in our framework do not require any pre-processing procedure on the data.

AB - We propose a 3D Constrained Local Model framework for deformable face alignment in depth image. Our framework exploits the intrinsic 3D geometric information in depth data by utilizing robust histogram-based 3D geometric features that are based on normal vectors. In addition, we demonstrate the fusion of intensity data and 3D features that further improves the facial landmark localization accuracy. The experiments are conducted on publicly available FRGC database. The results show that our 3D features based CLM completely outperforms the raw depth features based CLM in term of fitting accuracy and robustness, and the fusion of intensity and 3D depth feature further improves the performance. Another benefit is that the proposed 3D features in our framework do not require any pre-processing procedure on the data.

KW - EWI-25826

KW - HMI-HF: Human Factors

KW - deformable face alignment

KW - IR-95232

KW - Constrained Local Model

KW - 3D facial geometry

KW - METIS-309951

KW - histogram-based 3D feature

U2 - 10.1109/ICIP.2014.7025285

DO - 10.1109/ICIP.2014.7025285

M3 - Conference contribution

SN - 978-1-4799-5751-4

SP - 1425

EP - 1429

BT - Proceedings of IEEE International Conference on Image Processing (ICIP 2014)

PB - IEEE

CY - USA

T2 - IEEE International Conference on Image Processing, ICIP 2014

Y2 - 27 October 2014 through 30 October 2014

ER -