Facial expression recognition by using differential geometric features

Zangeneh, Erfan; Moradi, Aref

doi:10.1080/13682199.2018.1509176

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…From existing literature, it is found that geometric feature extraction techniques used for emotion recognition in FERS are normalised central moments descriptor (Ghimire et al, 2016b), AAM (Liliana et al, 2018;Zangeneh and Moradi, 2018), active shape model (Natarajan and Muthuswamy, 2014), point distribution model (Saeed et al, 2014), elastic bunch graph (Ghimire and Lee, 2013), T-stochastic embedding (Yu and Zhang, 2015), QuadTree Decomposition (Sandbach et al, 2012), Bezier Curve (Zhao-yi et al, 2010), local curvelet transform (Ucar et al, 2014) and canny edge detection (Vishnu, 2017). In this section, Figure 2 shows the overall framework of the FERS.…”

Section: Geometric Based Feature Extraction Techniquesmentioning

confidence: 99%

Systematic review of various feature extraction techniques for facial emotion recognition system

Kumari¹,

Bhatia²

2021

IJIEI

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Facial emotion recognition (FER) is the task of recognising human emotions from images and videos. Communicating through facial emotions is a kind of non-verbal communication and it reflects a person's inner thoughts and mental states. In the present study, various existing geometric and appearance based feature extraction techniques used in FER are reviewed in tabular form. The main motive of this paper is to analyse the performance of these techniques on the bases of accuracy on different datasets like JAFFE, CK+, CK and MMI. After extensive research on feature extraction techniques for FERS, it is found that the appearance feature-based techniques achieved maximum accuracy and more favourable as compared to geometric feature-based techniques. Finally, the paper concludes with the various challenges encountered for feature extraction in the field of FERS which need to be addressed in the future.

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Section: Geometric Based Feature Extraction Techniquesmentioning

confidence: 99%

Systematic review of various feature extraction techniques for facial emotion recognition system

Kumari¹,

Bhatia²

2021

IJIEI

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“…ey employed ASM to extract different facial expression regions. Similarly, Zangeneh and Moradi [9] first used the active appearance model (AAM) to reveal the important facial points, and then differential geometric features are extracted from those facial points. In the geometric-based features extraction techniques, it is difficult to track and initialize facial feature points in real time.…”

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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“…Statistical methods, Gabor wavelet, and local binary method belong to the feature extraction of static images [8,9]. Geometric method, optical flow method, and model method belong to the feature extraction of dynamic images [10][11][12]. However, there are diversity and complexity in image acquisition.…”

Section: Introductionmentioning

confidence: 99%

Improved Facial Expression Recognition Method Based on GAN

Jun-huan

2021

Scientific Programming

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Recognizing facial expressions accurately and effectively is of great significance to medical and other fields. Aiming at problem of low accuracy of face recognition in traditional methods, an improved facial expression recognition method is proposed. The proposed method conducts continuous confrontation training between the discriminator structure and the generator structure of the generative adversarial networks (GANs) to ensure enhanced extraction of image features of detected data set. Then, the high-accuracy recognition of facial expressions is realized. To reduce the amount of calculation, GAN generator is improved based on idea of residual network. The image is first reduced in dimension and then processed to ensure the high accuracy of the recognition method and improve real-time performance. Experimental part of the thesis uses JAFEE dataset, CK + dataset, and FER2013 dataset for simulation verification. The proposed recognition method shows obvious advantages in data sets of different sizes. The average recognition accuracy rates are 96.6%, 95.6%, and 72.8%, respectively. It proves that the method proposed has a generalization ability.

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Facial expression recognition by using differential geometric features

Cited by 10 publications

References 6 publications

Systematic review of various feature extraction techniques for facial emotion recognition system

Systematic review of various feature extraction techniques for facial emotion recognition system

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

Improved Facial Expression Recognition Method Based on GAN

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