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Robust facial expression recognition via compressive sensing.

Zhang S, Zhao X, Lei B - Sensors (Basel) (2012)

Bottom Line: The CS theory is used to construct a sparse representation classifier (SRC).The effectiveness and robustness of the SRC method is investigated on clean and occluded facial expression images.Three typical facial features, i.e., the raw pixels, Gabor wavelets representation and local binary patterns (LBP), are extracted to evaluate the performance of the SRC method.

View Article: PubMed Central - PubMed

Affiliation: School of Physics and Electronic Engineering, Taizhou University, Taizhou 318000, China. tzczsq@163.com

ABSTRACT
Recently, compressive sensing (CS) has attracted increasing attention in the areas of signal processing, computer vision and pattern recognition. In this paper, a new method based on the CS theory is presented for robust facial expression recognition. The CS theory is used to construct a sparse representation classifier (SRC). The effectiveness and robustness of the SRC method is investigated on clean and occluded facial expression images. Three typical facial features, i.e., the raw pixels, Gabor wavelets representation and local binary patterns (LBP), are extracted to evaluate the performance of the SRC method. Compared with the nearest neighbor (NN), linear support vector machines (SVM) and the nearest subspace (NS), experimental results on the popular Cohn-Kanade facial expression database demonstrate that the SRC method obtains better performance and stronger robustness to corruption and occlusion on robust facial expression recognition tasks.

No MeSH data available.


Related in: MedlinePlus

The process of LBP features extraction
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f2-sensors-12-03747: The process of LBP features extraction

Mentions: The process of LBP features extraction is summarized as follows: firstly, a facial image is divided into several non-overlapping blocks. Secondly, LBP histograms are computed for each block. Finally, the block LBP histograms are concatenated into a single vector. As a result, the facial image is represented by the LBP code. Figure 2 presents the process of LBP features extraction.


Robust facial expression recognition via compressive sensing.

Zhang S, Zhao X, Lei B - Sensors (Basel) (2012)

The process of LBP features extraction
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376615&req=5

f2-sensors-12-03747: The process of LBP features extraction
Mentions: The process of LBP features extraction is summarized as follows: firstly, a facial image is divided into several non-overlapping blocks. Secondly, LBP histograms are computed for each block. Finally, the block LBP histograms are concatenated into a single vector. As a result, the facial image is represented by the LBP code. Figure 2 presents the process of LBP features extraction.

Bottom Line: The CS theory is used to construct a sparse representation classifier (SRC).The effectiveness and robustness of the SRC method is investigated on clean and occluded facial expression images.Three typical facial features, i.e., the raw pixels, Gabor wavelets representation and local binary patterns (LBP), are extracted to evaluate the performance of the SRC method.

View Article: PubMed Central - PubMed

Affiliation: School of Physics and Electronic Engineering, Taizhou University, Taizhou 318000, China. tzczsq@163.com

ABSTRACT
Recently, compressive sensing (CS) has attracted increasing attention in the areas of signal processing, computer vision and pattern recognition. In this paper, a new method based on the CS theory is presented for robust facial expression recognition. The CS theory is used to construct a sparse representation classifier (SRC). The effectiveness and robustness of the SRC method is investigated on clean and occluded facial expression images. Three typical facial features, i.e., the raw pixels, Gabor wavelets representation and local binary patterns (LBP), are extracted to evaluate the performance of the SRC method. Compared with the nearest neighbor (NN), linear support vector machines (SVM) and the nearest subspace (NS), experimental results on the popular Cohn-Kanade facial expression database demonstrate that the SRC method obtains better performance and stronger robustness to corruption and occlusion on robust facial expression recognition tasks.

No MeSH data available.


Related in: MedlinePlus