A new method for facial expression recognition based on sparse representation plus LBP

Huang, Mianliang; Wang, Zhewei; Ying, Zilu

doi:10.1109/cisp.2010.5647898

Cited by 65 publications

(42 citation statements)

References 12 publications

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“…The first step is called patterns "uniform" [26] and the generation of it may call LBP map. But the results in [22] show that LBP + SRC can only perform an accuracy rate of 62.9%. As mentioned in [2], different facial expression is composed of different action units.…”

Section: The Role Of Feature Extraction Within Src For Fermentioning

confidence: 76%

“…Compared with traditional classification methods such as nearest neighbor (NN) [19], nearest subspace (NS) [20] and support vector machine (SVM), SRC framework shows robust performance on occlusion and corruption. In few literatures, SRC is used to conjunction with different features for FER [21][22][23][24]. The results in these papers show that SRC is better than NN and SVM using the same feature transformation.…”

Section: Introductionmentioning

confidence: 98%

“…The results in these papers show that SRC is better than NN and SVM using the same feature transformation. For instance, [21] shows DS + SRC gives better performance than DS + SVM, Eigen + NN and Gabor + NN while dealing with clean and noised images; [22] shows LBP + SRC method is more efficient than PCA, LDA, and Gabor histogram feature + MVBoost; [24] shows 2DPCA + SRC method gives better performance than 2DPCA + SVM. In face recognition, SRC framework performs well even regardless of feature extraction method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust automatic facial expression detection method based on sparse representation plus LBP map

Ouyang

Sang

Huang

2013

Optik

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Section: The Role Of Feature Extraction Within Src For Fermentioning

confidence: 76%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust automatic facial expression detection method based on sparse representation plus LBP map

Ouyang

Sang

Huang

2013

Optik

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“…Recently, the advantages of exploiting sparsity in pattern classification have been extensively demonstrated in [2], [3], [4], [5], [6], [7], [28]. In [2], Wright et al suggested a framework for face recognition (FR) using sparse representation.…”

Section: Introductionmentioning

confidence: 98%

“…The experimental results of [2] showed that the sparse representation based classifier (SRC) was superior to other widely used classifiers under challenging FR conditions. Inspired by the successful use in FR, SRC was studied for the purpose of FER in [4], [5], [6], [7], [28]. Most of the FER methods based on SRC made use of features which capture intensity changes of face appearance itself (i.e., appearance based feature).…”

Section: Introductionmentioning

confidence: 99%

Intra-Class Variation Reduction Using Training Expression Images for Sparse Representation Based Facial Expression Recognition

Lee

Plataniotis

2014

IEEE Trans. Affective Comput.

110

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Automatic facial expression recognition (FER) is becoming increasingly important in the area of affective computing systems because of its various emerging applications such as human-machine interface and human emotion analysis. Recently, sparse representation based FER has become popular and has shown an impressive performance. However, sparse representation could often produce less meaningful sparse solution for FER due to intra-class variation such as variation in identity or illumination. This paper proposes a new sparse representation based FER method, aiming to reduce the intra-class variation while emphasizing the facial expression in a query face image. To that end, we present a new method for generating an intra-class variation image of each expression by using training expression images. The appearance of each intra-class variation image could be close to the appearance of the query face image in identity and illumination. Therefore, the differences between the query face image and its intra-class variation images are used as the expression features for sparse representation. Experimental results show that the proposed FER method has high discriminating capability in terms of improving FER performance. Further, the intra-class variation images of non-neutral expressions are complementary with that of neutral expression, for improving FER performance.

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Autonomous Facial Expression Detection and Human-Robot-Interaction: A Survey

Atone

Bhalchandra

2021

Lecture Notes in Networks and Systems

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A new method for facial expression recognition based on sparse representation plus LBP

Cited by 65 publications

References 12 publications

Robust automatic facial expression detection method based on sparse representation plus LBP map

Robust automatic facial expression detection method based on sparse representation plus LBP map

Intra-Class Variation Reduction Using Training Expression Images for Sparse Representation Based Facial Expression Recognition

Autonomous Facial Expression Detection and Human-Robot-Interaction: A Survey

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