Facial Expression Recognition Based on Fusion of Sparse Representation

Ying, Zilu; Wang, Zhewei; Huang, Mianliang

doi:10.1007/978-3-642-14932-0_57

Cited by 25 publications

(20 citation statements)

References 10 publications

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“…These features can be human-crafted including Gabor wavelet coefficients [37,36,25,1], Haar features [27,30], Histograms of Oriented Gradients (HOG) [9,4], histograms of Local Binary Patterns (LBP) [38,26,22], or learned in a data-driven manner including sparse-coding based approaches [33,8,31,14,16,2,39,32,15] and deep learning framework [21].…”

Section: Related Workmentioning

confidence: 99%

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Feature Disentangling Machine - A Novel Approach of Feature Selection and Disentangling in Facial Expression Analysis

Liu

Zhou

Tsang

et al. 2014

Computer Vision – ECCV 2014

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Abstract. Studies in psychology show that not all facial regions are of importance in recognizing facial expressions and different facial regions make different contributions in various facial expressions. Motivated by this, a novel framework, named Feature Disentangling Machine (FDM), is proposed to effectively select active features characterizing facial expressions. More importantly, the FDM aims to disentangle these selected features into non-overlapped groups, in particular, common features that are shared across different expressions and expression-specific features that are discriminative only for a target expression. Specifically, the FDM integrates sparse support vector machine and multi-task learning in a unified framework, where a novel loss function and a set of constraints are formulated to precisely control the sparsity and naturally disentangle active features. Extensive experiments on two well-known facial expression databases have demonstrated that the FDM outperforms the state-of-the-art methods for facial expression analysis. More importantly, the FDM achieves an impressive performance in a crossdatabase validation, which demonstrates the generalization capability of the selected features.

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Section: Related Workmentioning

confidence: 99%

“…based feature learning approaches [33,8,31,14,16,2,39,32] have been employed to extract underlying "edge-like" features from facial images. Furthermore, the evocation of different expressions may involve the muscular movements from the same facial regions, which could be treated as common features across different expressions.…”

Section: Figmentioning

confidence: 99%

Feature Disentangling Machine - A Novel Approach of Feature Selection and Disentangling in Facial Expression Analysis

Liu

Zhou

Tsang

et al. 2014

Computer Vision – ECCV 2014

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“…There are several past and current efforts in modeling and recognizing the nonrigid deformations in facial components as a result of various expressions. Some of these approaches extract a set of appearancebased features, including raw gray scale intensities [26], [27], Gabor features [28], [29], [30], [31], Haar-like features [8], [32] and local binary patterns (LBP) [33], [27] from a still image or use some rules based on the deformation of local facial components and shape-based features [34], [35], [36], [37], [9] and then employ a machine learning approach, such as Adaboost [8], SVM [38], [10] and Neural Nets [39] for expression recognition.…”

Section: Identity-independent Expression Recognitionmentioning

confidence: 99%

“…The ideas of sparse representation have also been applied to facial expression recognition [31], [27]. Ying et al [27] designed two classifiers in the sparse domain using two different sets of image features: raw gray scale pixel values and local binary patterns.…”

Section: Identity-independent Expression Recognitionmentioning

confidence: 99%

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Component-Based Recognition of Facesand Facial Expressions

Taheri¹,

Patel

Chellappa

2013

IEEE Trans. Affective Comput.

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Abstract-Most of the existing methods for the recognition of faces and expressions consider either the expression-invariant face recognition problem or the identity-independent facial expression recognition problem. In this paper, we propose joint face and facial expression recognition using a dictionary-based component separation algorithm (DCS). In this approach, the given expressive face is viewed as a superposition of a neutral face component with a facial expression component which is sparse with respect to the whole image. This assumption leads to a dictionary-based component separation algorithm which benefits from the idea of sparsity and morphological diversity. This entails building data-driven dictionaries for neutral and expressive components. The DCS algorithm then uses these dictionaries to decompose an expressive test face into its constituent components. The sparse codes we obtain as a result of this decomposition are then used for joint face and expression recognition. Experiments on publicly available expression and face datasets show the effectiveness of our method.

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