Facial expression recognition using a pairwise feature selection and classification approach

Cossetin, Marcelo J.; Nievola, Júlio César; Koerich, Alessandro L.

doi:10.1109/ijcnn.2016.7727879

Cited by 42 publications

(22 citation statements)

References 21 publications

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“…The experimental results have shown the great impact of transfer learning in our model for the video modality. In the multimodal fusion, recent studies have shown that despite the fact that new and exotic fusion strategies are being developed, traditional fusion schemes are still able to generate competitive results [2], [4], [7], [8].…”

Section: Discussionmentioning

confidence: 99%

Emotion Recognition Using Fusion of Audio and Video Features

Ortega

Cardinal

Koerich

2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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In this paper we propose a fusion approach to continuous emotion recognition that combines visual and auditory modalities in their representation spaces to predict the arousal and valence levels. The proposed approach employs a pre-trained convolution neural network and transfer learning to extract features from video frames that capture the emotional content. For the auditory content, a minimalistic set of parameters such as prosodic, excitation, vocal tract, and spectral descriptors are used as features. The fusion of these two modalities is carried out at a feature level, before training a single support vector regressor (SVR) or at a prediction level, after training one SVR for each modality. The proposed approach also includes preprocessing and postprocessing techniques which contribute favorably to improving the concordance correlation coefficient (CCC). Experimental results for predicting spontaneous and natural emotions on the RECOLA dataset have shown that the proposed approach takes advantage of the complementary information of visual and auditory modalities and provides CCCs of 0.749 and 0.565 for arousal and valence, respectively.

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Section: Discussionmentioning

confidence: 99%

Emotion Recognition Using Fusion of Audio and Video Features

Ortega

Cardinal

Koerich

2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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“…What makes FAST do not perform well, is due to the ratio of detected keypoints to facial keypoints and additionally, the characteristic of ORL Database that has more noise than Head Pose's image. According to the research of [30], although FAST can detect more accurately, FAST is more sensitive to noise.…”

Section: Methodsmentioning

confidence: 99%

“…The objective of Histogram Equalization is to spread gray levels over the entire allowable range, which is nonlinear and irreversible [21]. It has been mostly used to increase the contrast in face recognition studies [28], [29], [30], [15].…”

Section: B Histogram Equalizationmentioning

confidence: 99%

The Effect of Using Histogram Equalization and Discrete Cosine Transform on Facial Keypoint Detection

Kusnadi

Darmawan

Pane

et al. 2020

EECSI

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This study aims to figure out the effect of using Histogram Equalization and Discrete Cosine Transform (DCT) in detecting facial keypoints, which can be applied for 3D facial reconstruction in face recognition. Four combinations of methods comprising of Histogram Equalization, removing lowfrequency coefficients using Discrete Cosine Transform (DCT) and using five feature detectors, namely: SURF, Minimum Eigenvalue, Harris-Stephens, FAST, and BRISK were used for test. Data that were used for test were obtained from Head Pose Image and ORL Databases. The result from the test were evaluated using F-score. The highest F-score for Head Pose Image Dataset is 0.140 and achieved through the combination of DCT & Histogram Equalization with feature detector SURF. The highest F-score for ORL Database is 0.33 and achieved through the combination of DCT & Histogram Equalization with feature detector BRISK.

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“…A convolutional neural network (CNN) was used [15]. In another study [16], the authors of that report used pairwise feature selection in classification. Multiple CNNs were used in other research [17].…”

Section: Related Workmentioning

confidence: 99%

Facial expression recognition using three-stage support vector machines

Dagher

Dahdah

Shakik

2019

Vis. Comput. Ind. Biomed. Art

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Herein, a three-stage support vector machine (SVM) for facial expression recognition is proposed. The first stage comprises 21 SVMs, which are all the binary combinations of seven expressions. If one expression is dominant, then the first stage will suffice; if two are dominant, then the second stage is used; and, if three are dominant, the third stage is used. These multilevel stages help reduce the possibility of experiencing an error as much as possible. Different image preprocessing stages are used to ensure that the features attained from the face detected have a meaningful and proper contribution to the classification stage. Facial expressions are created as a result of muscle movements on the face. These subtle movements are detected by the histogram-oriented gradient feature, because it is sensitive to the shapes of objects. The features attained are then used to train the three-stage SVM. Two different validation methods were used: the leave-one-out and K-fold tests. Experimental results on three databases (Japanese Female Facial Expression, Extended Cohn-Kanade Dataset, and Radboud Faces Database) show that the proposed system is competitive and has better performance compared with other works.

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Facial expression recognition using a pairwise feature selection and classification approach

Cited by 42 publications

References 21 publications

Emotion Recognition Using Fusion of Audio and Video Features

Emotion Recognition Using Fusion of Audio and Video Features

The Effect of Using Histogram Equalization and Discrete Cosine Transform on Facial Keypoint Detection

Facial expression recognition using three-stage support vector machines

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