Facial expression recognition based on adaptive local binary pattern and sparse representation

Wang, Zhen; Ying, Zilu

doi:10.1109/csae.2012.6272810

Cited by 13 publications

(18 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…In [5], it was shown that SRC outperformed nearest neighbor (NN) [10] and support vector machine (SVM) classifiers [11], independent of features used (e.g., raw pixel or LBP or Gabor wavelet [9]). In [6] and [28], the effectiveness of SRC with appearance based features such as two-dimensional principal component analysis (2DPCA) [12] and local phase quantization (LPQ) [24] was experimentally verified. In the previous works in [4], [5], [6], [28], the authors showed that SRC could achieve some improvements in FER performance by using appearance based features.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Lee

Plataniotis

2014

IEEE Trans. Affective Comput.

110

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Lee

Plataniotis

2014

IEEE Trans. Affective Comput.

110

View full text Add to dashboard Cite

show abstract

“…LP Q descriptor has gained increasing attention due to its simplicity and excellent perfonnance in various texture analysis applications. And sparse representation and LPQ have proved to be a very effective approach in FER [4]. Gabor wavelet method is a multi -resolution description, which has the good feature extraction characteristics and not sensitivity to light and position.…”

Section: Introductionmentioning

confidence: 99%

Facial expression recognition by fusion of gabor texture features and local phase quantization

Ying

Yang

2014

2014 12th International Conference on Signal Processing (ICSP)

View full text Add to dashboard Cite

In this paper, we proposed a novel algorithm for Facial Expression Recognition (FER) which was based on fusion of gabor texture features and Local Phase Quantization (LPQ) . Firstly, the LPQ feature and gabor texture feature were respectively extracted from every expression image. LPQ features are histograms of LPQ transfonn. Five scales and eight orientations of gabor wavelet filters are used to extract gabor texture features and adaboost algorithm is used to select gabor features. Then we obtain two expression recognition results on both expression features by Sparse Representation based Classification (SRC) method. Finally, the final expression recognition was performed by fusion of residuals of two SRC algorithms. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrated that the new algorithm was better than the original two algorithms, and this algorithm had a much higher recognition rate than the traditional algorithm.

show abstract

“…Liu et al (2012) [48] has been carried out facial expression recognition based on the fusion of geometry features and texture features. Deng et al [52] demonstrated about local Gabor based feature extraction with LDA and PCA projection. Zhen and Zilu [53] tested JAFFE database using fusion approach which was framed by Gabor filter and 70% of overall recognition accuracy has been achieved.…”

Section: Introductionmentioning

confidence: 99%

Subspace based Expression Recognition Using Combinational Gabor based Feature Fusion

Hegde¹,

Seetha²

2017

IJIGSP

View full text Add to dashboard Cite

Abstract-This paper demonstrates mainly on enhancement of extracted feature and proposes a novel approach for feature level fusion for efficient expression recognition. Extracted Gabor filter magnitude feature vector has been fused with upper face part geometrical features and Gabor phase feature vector has been fused with lower face part geometrical features respectively. Both these high dimensional feature dataset have been projected into low dimensional subspace for decorrelating the feature data redundancy by preserving local and global discriminative features of various expression classes of JAFFE, YALE and FD databases. The effectiveness of subspace of fused dataset has been measured with different dimensional parameters of Gabor filter. The experimental results reveal that performance of the subspace approaches for high dimensional proposed feature level fused dataset yields higher accuracy rates compared to state of art approaches.

show abstract

Facial expression recognition based on adaptive local binary pattern and sparse representation

Cited by 13 publications

References 9 publications

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

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

Facial expression recognition by fusion of gabor texture features and local phase quantization

Subspace based Expression Recognition Using Combinational Gabor based Feature Fusion

Contact Info

Product

Resources

About