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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

Examples of facial expression images from the Cohn-Kanade database.
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f3-sensors-12-03747: Examples of facial expression images from the Cohn-Kanade database.

Mentions: The Cohn-Kanade database [34] consists of 100 university students aged from 18 to 30 years, of which 65% were female, 15% were African-American and 3% were Asian or Latino. Subjects were instructed to perform a series of 23 facial displays, six of which were based on description of prototypic emotions. Image sequences from neutral to target display were digitized into 640 × 490 pixels with 8-bit precision for grayscale values. Figure 3 shows some sample images from the Cohn-Kanade database. In this work, 320 image sequences were selected from the Cohn-Kanade database. The selected sequences, each of which could be labeled as one of the six basic emotions, come from 96 subjects, with 1 to 6 emotions per subject. For each sequence, the neutral face and one peak frames were used for prototypic expression recognition. Finally, 470 images (32 anger, 100 joy, 55 sadness, 75 surprise, 47 fear, 45 disgust and 116 neutral) were obtained for experiments.


Robust facial expression recognition via compressive sensing.

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

Examples of facial expression images from the Cohn-Kanade database.
© Copyright Policy
Related In: Results  -  Collection

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

f3-sensors-12-03747: Examples of facial expression images from the Cohn-Kanade database.
Mentions: The Cohn-Kanade database [34] consists of 100 university students aged from 18 to 30 years, of which 65% were female, 15% were African-American and 3% were Asian or Latino. Subjects were instructed to perform a series of 23 facial displays, six of which were based on description of prototypic emotions. Image sequences from neutral to target display were digitized into 640 × 490 pixels with 8-bit precision for grayscale values. Figure 3 shows some sample images from the Cohn-Kanade database. In this work, 320 image sequences were selected from the Cohn-Kanade database. The selected sequences, each of which could be labeled as one of the six basic emotions, come from 96 subjects, with 1 to 6 emotions per subject. For each sequence, the neutral face and one peak frames were used for prototypic expression recognition. Finally, 470 images (32 anger, 100 joy, 55 sadness, 75 surprise, 47 fear, 45 disgust and 116 neutral) were obtained for experiments.

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