Multi-view facial expression recognition

Hu, Yuxiao; Zeng, Zhihong; Liu, Yin; Wei, Xiaozhou; Zhou, Xi; Huang, Thomas S.

doi:10.1109/afgr.2008.4813445

Cited by 106 publications

(70 citation statements)

References 13 publications

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“…More recently two studies have explored facial expression recognition with varying yaw angles on the BU-3DEF database [7,8]. Hu et al [8] focuses on facial expression recognition using LBPs, Histograms of Oriented Gradients (HOGs) and the Scale Invariant Feature Transform (SIFT) to characterize facial expressions over 5 yaw rotation angles from frontal to profile views.…”

Section: Related Workmentioning

confidence: 99%

“…Hu et al [8] focuses on facial expression recognition using LBPs, Histograms of Oriented Gradients (HOGs) and the Scale Invariant Feature Transform (SIFT) to characterize facial expressions over 5 yaw rotation angles from frontal to profile views. Other contributions of this work are the strong performance increase when features are combined with Locality Preserving Projection (LPP).…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we explore this question using an appearance based approach. Noticeable limitations in the work of [8] and [7] are that features are extracted using a set of sparse manually labeled feature points. We adopt a dense …”

Section: Related Workmentioning

confidence: 99%

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The effects of Pose on Facial Expression Recognition

Moore¹,

Bowden²

2009

Procedings of the British Machine Vision Conference 2009

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Research into facial expression recognition has predominantly been based upon near frontal view data. However, a recent 3D facial expression database (BU-3DFE database) has allowed empirical investigation of facial expression recognition across pose. In this paper, we investigate the effects of pose from frontal to profile view on facial expression recognition. Experiments are carried out on 100 subjects with 5 yaw angles over 6 prototypical expressions. Expressions have 4 levels of intensity from subtle to exaggerated. We evaluate features such as local binary patterns (LBPs) as well as various extensions of LBPs. In addition, a novel approach to facial expression recognition is proposed using local gabor binary patterns (LGBPs). Multi class support vector machines (SVMs) are used for classification. We investigate the effects of image resolution and pose on facial expression classification using a variety of different features.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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The effects of Pose on Facial Expression Recognition

Moore¹,

Bowden²

2009

Procedings of the British Machine Vision Conference 2009

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“…In challenging domains such as facial expression recognition, algorithms can capitalise on precise fiducial facial point localisation by extracting appearance features from locations relative to these points (e.g. [17], [35]) or by using the locations of the points directly (e.g. [34], [38]).…”

Section: Introductionmentioning

confidence: 99%

Local Evidence Aggregation for Regression-Based Facial Point Detection

Martínez

Valstar

Binefa

et al. 2013

IEEE Trans. Pattern Anal. Mach. Intell.

119

103

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Abstract-We propose a new algorithm to detect facial points in frontal and near-frontal face images. It combines a regressionbased approach with a probabilistic graphical model-based face shape model, that restricts the search to anthropomorphically consistent regions. While most regression-based approaches perform a sequential approximation of the target location, our algorithm detects the target location by aggregating the estimates obtained from stochastically selected local appearance information into a single robust prediction. The underlying assumption is that by aggregating the different estimates, their errors will cancel out as long as the regressor inputs are uncorrelated. Once this new perspective is adopted, the problem is reformulated as how to optimally select the test locations over which the regressors are evaluated. We propose to extend the regressionbased model to provide a quality measure of each prediction, and use the shape model to restrict and correct the sampling region. Our approach combines the low computational cost typical of regression-based approaches with the robustness of exhaustive-search approaches. The proposed algorithm was tested on over 7,500 images from 5 databases. Results showed significant improvement over the current state of the art.

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“…The view-dependent approach is a general framework to recognize facial expressions on arbitrary views [7,8,13,15,16]. It consists of 2D pose estimation and emotion classification.…”

Section: Introductionmentioning

confidence: 99%

Emotion recognition from facial images with arbitrary views

Huang

Zhao

Pietikäinen

2013

Procedings of the British Machine Vision Conference 2013

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Facial expression recognition has been predominantly utilized to analyze the emotional status of human beings. In practice nearly frontal-view facial images may not be available. Therefore, a desirable property of facial expression recognition would allow the user to have any head pose. Some methods on non-frontal-view facial images were recently proposed to recognize the facial expressions by building discriminative subspace in specific views. We argue that this kind of approach ignores (1) the discrimination of inter-class samples with the same view label and (2) the closeness of intra-class samples with all view labels. This paper proposes a new method to recognize arbitrary-view facial expressions by using discriminative neighborhood preserving embedding and multi-view concepts. It first captures the discriminative property of inter-class samples. In addition, it explores the closeness of intra-class samples with arbitrary view in a low-dimensional subspace. Experimental results on BU-3DFE and Multi-PIE databases show that our approach achieves promising results for recognizing facial expressions with arbitrary views.

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Multi-view facial expression recognition

Cited by 106 publications

References 13 publications

The effects of Pose on Facial Expression Recognition

The effects of Pose on Facial Expression Recognition

Local Evidence Aggregation for Regression-Based Facial Point Detection

Emotion recognition from facial images with arbitrary views

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