Facial expression recognition using a combination of multiple facial features and support vector machine

Tsai, Hung-Hsu; Chang, Yung‐Chen

doi:10.1007/s00500-017-2634-3

Cited by 73 publications

(24 citation statements)

References 23 publications

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“…The SVM [41,[65][66][67][68][69] can find a good compromising solution on complex models by providing limited sample data information to obtain generalisation ability. It is also possible to map linearly indivisible data to higher dimensions by kernel functions to convert the data into linear separable.…”

Section: Expression Classificationmentioning

confidence: 99%

Facial Expression Recognition: A Survey

et al. 2019

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Facial Expression Recognition (FER), as the primary processing method for non-verbal intentions, is an important and promising field of computer vision and artificial intelligence, and one of the subject areas of symmetry. This survey is a comprehensive and structured overview of recent advances in FER. We first categorise the existing FER methods into two main groups, i.e., conventional approaches and deep learning-based approaches. Methodologically, to highlight the differences and similarities, we propose a general framework of a conventional FER approach and review the possible technologies that can be employed in each component. As for deep learning-based methods, four kinds of neural network-based state-of-the-art FER approaches are presented and analysed. Besides, we introduce seventeen commonly used FER datasets and summarise four FER-related elements of datasets that may influence the choosing and processing of FER approaches. Evaluation methods and metrics are given in the later part to show how to assess FER algorithms, along with subsequent performance comparisons of different FER approaches on the benchmark datasets. At the end of the survey, we present some challenges and opportunities that need to be addressed in future.

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Section: Expression Classificationmentioning

confidence: 99%

Facial Expression Recognition: A Survey

et al. 2019

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“…Exploring shape deformation is done [13]. In other work [14], SVM was used in FER. A convolutional neural network (CNN) was used [15].…”

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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“…ey also proposed an improved random forest classifier for effective and efficient recognition of facial expressions. In the method of Tsai and Chang [22], features are extracted via Gabor filter, discrete cosine transform, and angular radial transform. In the work of Ghimire et al [23], first, the face local specific regions were selected, and then central moments were normalized.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Facial Expressions under Varying Conditions Using Dual‐Feature Fusion

Mahmood

Hussain

Iqbal

et al. 2019

Mathematical Problems in Engineering

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Facial expression recognition plays an important role in communicating the emotions and intentions of human beings. Facial expression recognition in uncontrolled environment is more difficult as compared to that in controlled environment due to change in occlusion, illumination, and noise. In this paper, we present a new framework for effective facial expression recognition from real-time facial images. Unlike other methods which spend much time by dividing the image into blocks or whole face image, our method extracts the discriminative feature from salient face regions and then combine with texture and orientation features for better representation. Furthermore, we reduce the data dimension by selecting the highly discriminative features. The proposed framework is capable of providing high recognition accuracy rate even in the presence of occlusions, illumination, and noise. To show the robustness of the proposed framework, we used three publicly available challenging datasets. The experimental results show that the performance of the proposed framework is better than existing techniques, which indicate the considerable potential of combining geometric features with appearance-based features.

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Facial expression recognition using a combination of multiple facial features and support vector machine

Cited by 73 publications

References 23 publications

Facial Expression Recognition: A Survey

Facial Expression Recognition: A Survey

Facial expression recognition using three-stage support vector machines

Recognition of Facial Expressions under Varying Conditions Using Dual‐Feature Fusion

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