Automatic facial expression recognition: feature extraction and selection

Lajevardi, Seyed Mehdi; Hussain, Zahir M.

doi:10.1007/s11760-010-0177-5

Cited by 99 publications

(46 citation statements)

References 19 publications

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“…As a future direction, we are currently working on using SSIM as a tool for face recognition, where initial results are promising. Also, an extension towards facial expression recognition as per (Lajevardi and Hussain, 2012;) is under consideration.…”

Section: Resultsmentioning

confidence: 99%

Novel Image-Dependent Quality Assessment Measures

Hashim¹

2014

Journal of Computer Science

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The image is a 2D signal whose pixels are highly correlated in a 2D manner. Hence, using pixel by pixel error what we called previously Mean-Square Error, (MSE) is not an efficient way to compare two similar images (e.g., an original image and a compressed version of it). Due to this correlation, image comparison needs a correlative quality measure. It is clear that correlation between two signals gives an idea about the relation between samples of the two signals. Generally speaking, correlation is a measure of similarity between the two signals. An important step in image similarity was introduced by Wang and Bovik where a structural similarity measure has been designed and called SSIM. The similarity measure SSIM has been widely used. It is based on statistical similarity between the two images. However, SSIM can produce confusing results in some cases where it may give a non-trivial amount of similarity while the two images are quite different. This study proposes methods to determine a reliable similarity between any two images, similar or dissimilar, in the sense that dissimilar images have near-zero similarity measure, while similar images give near-one (maximum) similarity. The proposed methods are based on image-dependent properties, specifically the outcomes of edge detection and segmentation, in addition to the statistical properties. The proposed methods are tested under Gaussian noise, impulse noise and blur, where good results have been obtained even under low Peak Signal-to-Noise Ratios (PSNR's).

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Section: Resultsmentioning

confidence: 99%

Novel Image-Dependent Quality Assessment Measures

Hashim¹

2014

Journal of Computer Science

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“…In this context, Lajevardi et al [12] employed HLAC and HLAClike features (HLACLF) for feature extraction and the Naive Bayesian classifier for classification. A more than 93% classification accuracy has been reported with Cohen-Kanade and JAFFE databases.…”

Section: Related Workmentioning

confidence: 99%

Region-Based Facial Expression Recognition in Still Images

Nagi¹,

Rahmat²,

Khalid³

et al. 2013

Journal of Information Processing Systems

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Abstract-In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.

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“…Adaboost has shown significant improvements over other algorithms [10], [13], [14]. mRMR also has been demonstrated with a better performance than PCA, mutual information, and genetic algorithm [5], [9]. Norm-based SVM has also shown good performance in recent work on object and action recognition [15].…”

Section: Related Workmentioning

confidence: 99%

“…There are many up-to-date studies that compare performance between different types of features [2], [3], [4], [5], [6], and selection algorithms [5], [7], [8], [9]. However, except for [4], [6], these studies have only benchmarked performance on posed emotions, rather than spontaneous ones.…”

Section: Introductionmentioning

confidence: 99%

“…These existing studies have only adopted either texture or geometry features, whereas when combined, they can be complementary. Few of them [2], [5] compare different features and different feature selection algorithms in the same framework. To the best of our knowledge, direct comparison between texture, geometry and their fusion, as well as between multiple selection algorithms has not been found for spontaneous FER.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discovering the Best Feature Extraction and Selection Algorithms for Spontaneous Facial Expression Recognition

Zhang

Tjondronegoro

Chandran

2012

2012 IEEE International Conference on Multimedia and Expo

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Feature extraction and selection are critical processes in developing facial expression recognition (FER) systems. While many algorithms have been proposed for these processes, direct comparison between texture, geometry and their fusion, as well as between multiple selection algorithms has not been found for spontaneous FER. This paper addresses this issue by proposing a unified framework for a comparative study on the widely used texture (LBP, Gabor and SIFT) and geometric (FAP) features, using Adaboost, mRMR and SVM feature selection algorithms. Our experiments on the Feedtum and NVIE databases demonstrate the benefits of fusing geometric and texture features, where SIFT+FAP shows the best performance, while mRMR outperforms Adaboost and SVM. In terms of computational time, LBP and Gabor perform better than SIFT. The optimal combination of SIFT+FAP+mRMR also exhibits a state-of-the-art performance.

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Automatic facial expression recognition: feature extraction and selection

Cited by 99 publications

References 19 publications

Novel Image-Dependent Quality Assessment Measures

Novel Image-Dependent Quality Assessment Measures

Region-Based Facial Expression Recognition in Still Images

Discovering the Best Feature Extraction and Selection Algorithms for Spontaneous Facial Expression Recognition

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