Regression-based Multi-View Facial Expression Recognition

Ognjen Rudovic; Ioannis Patras; Maja Pantic

doi:10.1109/ICPR.2010.1001

Regression-based Multi-View Facial Expression Recognition

Ognjen Rudovic
, Ioannis Patras
, Maja Pantic

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

40 Citations (Scopus)

179 Downloads (Pure)

Abstract

We present a regression-based scheme for multi-view facial expression recognition based on 2蚠D geometric features. We address the problem by mapping facial points (e.g. mouth corners) from non-frontal to frontal view where further recognition of the expressions can be performed using a state-of-the-art facial expression recognition method. To learn the mapping functions we investigate four regression models: Linear Regression (LR), Support Vector Regression (SVR), Relevance Vector Regression (RVR) and Gaussian Process Regression (GPR). Our extensive experiments on the CMU Multi- PIE facial expression database show that the proposed scheme outperforms view-specific classifiers by utilizing considerably less training data.

Original language	Undefined
Title of host publication	Proceedings of the 20th International Conference on Pattern Recognition, ICPR 2010
Place of Publication	USA
Publisher	IEEE
Pages	4121-4124
Number of pages	4
ISBN (Print)	978-0-7695-4109-9
DOIs	https://doi.org/10.1109/ICPR.2010.1001
Publication status	Published - 26 Aug 2010
Event	20th International Conference on Pattern Recognition 2010 - Istanbul Convention & Exhibition Centre, Istanbul, Turkey Duration: 23 Aug 2010 → 26 Aug 2010 Conference number: 20 https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=16097

Publication series

Name
Publisher	IEEE Computer Society

Conference

Conference	20th International Conference on Pattern Recognition 2010
Abbreviated title	ICPR 2010
Country/Territory	Turkey
City	Istanbul
Period	23/08/10 → 26/08/10
Internet address	https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=16097

Keywords

METIS-276346
IR-75896
EWI-19508
HMI-MI: MULTIMODAL INTERACTIONS
EC Grant Agreement nr.: FP7/211486

Access to Document

10.1109/ICPR.2010.1001

pantic_regression-based_multi-view

Cite this

@inproceedings{a4b561c7a2524327a0ff35c6106e0c31,

title = "Regression-based Multi-View Facial Expression Recognition",

abstract = "We present a regression-based scheme for multi-view facial expression recognition based on 2蚠D geometric features. We address the problem by mapping facial points (e.g. mouth corners) from non-frontal to frontal view where further recognition of the expressions can be performed using a state-of-the-art facial expression recognition method. To learn the mapping functions we investigate four regression models: Linear Regression (LR), Support Vector Regression (SVR), Relevance Vector Regression (RVR) and Gaussian Process Regression (GPR). Our extensive experiments on the CMU Multi- PIE facial expression database show that the proposed scheme outperforms view-specific classifiers by utilizing considerably less training data.",

keywords = "METIS-276346, IR-75896, EWI-19508, HMI-MI: MULTIMODAL INTERACTIONS, EC Grant Agreement nr.: FP7/211486",

author = "Ognjen Rudovic and Ioannis Patras and Maja Pantic",

note = "10.1109/ICPR.2010.1001 ; 20th International Conference on Pattern Recognition 2010 ; Conference date: 23-08-2010 Through 26-08-2010",

year = "2010",

month = aug,

day = "26",

doi = "10.1109/ICPR.2010.1001",

language = "Undefined",

isbn = "978-0-7695-4109-9",

publisher = "IEEE",

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booktitle = "Proceedings of the 20th International Conference on Pattern Recognition, ICPR 2010",

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T1 - Regression-based Multi-View Facial Expression Recognition

AU - Rudovic, Ognjen

AU - Patras, Ioannis

AU - Pantic, Maja

N1 - Conference code: 20

PY - 2010/8/26

Y1 - 2010/8/26

N2 - We present a regression-based scheme for multi-view facial expression recognition based on 2蚠D geometric features. We address the problem by mapping facial points (e.g. mouth corners) from non-frontal to frontal view where further recognition of the expressions can be performed using a state-of-the-art facial expression recognition method. To learn the mapping functions we investigate four regression models: Linear Regression (LR), Support Vector Regression (SVR), Relevance Vector Regression (RVR) and Gaussian Process Regression (GPR). Our extensive experiments on the CMU Multi- PIE facial expression database show that the proposed scheme outperforms view-specific classifiers by utilizing considerably less training data.

AB - We present a regression-based scheme for multi-view facial expression recognition based on 2蚠D geometric features. We address the problem by mapping facial points (e.g. mouth corners) from non-frontal to frontal view where further recognition of the expressions can be performed using a state-of-the-art facial expression recognition method. To learn the mapping functions we investigate four regression models: Linear Regression (LR), Support Vector Regression (SVR), Relevance Vector Regression (RVR) and Gaussian Process Regression (GPR). Our extensive experiments on the CMU Multi- PIE facial expression database show that the proposed scheme outperforms view-specific classifiers by utilizing considerably less training data.

KW - METIS-276346

KW - IR-75896

KW - EWI-19508

KW - HMI-MI: MULTIMODAL INTERACTIONS

KW - EC Grant Agreement nr.: FP7/211486

U2 - 10.1109/ICPR.2010.1001

DO - 10.1109/ICPR.2010.1001

M3 - Conference contribution

SN - 978-0-7695-4109-9

SP - 4121

EP - 4124

BT - Proceedings of the 20th International Conference on Pattern Recognition, ICPR 2010

PB - IEEE

CY - USA

T2 - 20th International Conference on Pattern Recognition 2010

Y2 - 23 August 2010 through 26 August 2010

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