A Delaunay-Based Temporal Coding Model for Micro-expression Recognition

Lu, Zhong-Rong; Luo, Ziqi; Zheng, Huicheng; Chen, Jikai; Li, Weihong

doi:10.1007/978-3-319-16631-5_51

Cited by 28 publications

(18 citation statements)

References 18 publications

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“…The proposed FACS-based regions follows a recent approach that moves away from blocking methods [32], [35], [36] and improves the local feature representation by disregarding areas of the face that do not contribute to facial muscle movements.…”

Section: Discussionmentioning

confidence: 99%

“…Delaunay triangulation has also been used to form regions on just the face and can exclude hair and neck [32], however this approach can still extract areas of the face that would not be useful as a feature and adds further computational expense.…”

Section: Related Work a Face Regionsmentioning

confidence: 99%

“…These regions have more focus on local parts of the face that move due to muscle activation. Some examples of ROI selection for micro-expression recognition and detection include discriminative response map fitting [35], Delaunay triangulation [32] and facial landmark based region selection [36]. Unfortunately, currently defined regions do not cover all AUs and miss some potentially important movements such as AU5 (Upper Lid Raiser), AU23 (Lip Tightener) and AU31 (Jaw Clencher).…”

Section: Related Work a Face Regionsmentioning

confidence: 99%

See 2 more Smart Citations

Objective Micro-Facial Movement Detection Using FACS-Based Regions and Baseline Evaluation

Davison

Merghani

Lansley³

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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Micro-facial expressions are regarded as an important human behavioural event that can highlight emotional deception. Spotting these movements is difficult for humans and machines, however research into using computer vision to detect subtle facial expressions is growing in popularity. This paper proposes an individualised baseline micro-movement detection method using 3D Histogram of Oriented Gradients (3D HOG) temporal difference method. We define a face template consisting of 26 regions based on the Facial Action Coding System (FACS). We extract the temporal features of each region using 3D HOG. Then, we use Chi-square distance to find subtle facial motion in the local regions. Finally, an automatic peak detector is used to detect micro-movements above the newly proposed adaptive baseline threshold. The performance is validated on two FACS coded datasets: SAMM and CASME II. This objective method focuses on the movement of the 26 face regions. When comparing with the ground truth, the best result was an AUC of 0.7512 and 0.7261 on SAMM and CASME II, respectively. The results show that 3D HOG outperformed for micro-movement detection, compared to state-of-the-art feature representations: Local Binary Patterns in Three Orthogonal Planes and Histograms of Oriented Optical Flow.

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Section: Discussionmentioning

confidence: 99%

Section: Related Work a Face Regionsmentioning

confidence: 99%

Section: Related Work a Face Regionsmentioning

confidence: 99%

See 1 more Smart Citation

Objective Micro-Facial Movement Detection Using FACS-Based Regions and Baseline Evaluation

Davison

Merghani

Lansley³

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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“…However, by splitting into m × n blocks, the chance of introducing irrelevant facial features is higher. Recent approaches to this problem have used Delaunay triangulation [46] and specifically chosen regions of interest using facial feature points to define the region boundaries for analysis [27]. For detecting micro-movements, the 'noise' and unwanted data captured on the face, like head and eye movements, need to be minimised through a better definition of face regions based on FACS.…”

Section: Future Workmentioning

confidence: 99%

SAMM: A Spontaneous Micro-Facial Movement Dataset

Davison

Lansley²,

Costen

et al. 2018

IEEE Trans. Affective Comput.

415

237

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Micro-facial expressions are spontaneous, involuntary movements of the face when a person experiences an emotion but attempts to hide their facial expression, most likely in a high-stakes environment. Recently, research in this field has grown in popularity, however publicly available datasets of micro-expressions have limitations due to the difficulty of naturally inducing spontaneous micro-expressions. Other issues include lighting, low resolution and low participant diversity. We present a newly developed spontaneous micro-facial movement dataset with diverse participants and coded using the Facial Action Coding System. The experimental protocol addresses the limitations of previous datasets, including eliciting emotional responses from stimuli tailored to each participant. Dataset evaluation was completed by running preliminary experiments to classify micro-movements from non-movements. Results were obtained using a selection of spatio-temporal descriptors and machine learning. We further evaluate the dataset on emerging methods of feature difference analysis and propose an Adaptive Baseline Threshold that uses individualised neutral expression to improve the performance of micro-movement detection. In contrast to machine learning approaches, we outperform the state of the art with a recall of 0.91. The outcomes show the dataset can become a new standard for micro-movement data, with future work expanding on data representation and analysis.

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“…In [35], facial feature points have been tracked and used to recognize specific micro-expressions (i.e., happiness and disgust). Furthermore, the Delaunay triangulation based on extracted landmarks was used to reveal subtle muscle movements [36] and encoded temporally for dynamical microexpressions. Besides, based on optical flow estimation, some approaches leverage the magnitude, orientation and other highorder statistics to model the dynamics of micro-expressions.…”

Section: A Micro-expression Analysismentioning

confidence: 99%

Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-Expressions

Xia

Hong

Gao

et al. 2020

IEEE Trans. Multimedia

191

130

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Recently, the recognition task of spontaneous facial micro-expressions has attracted much attention with its various real-world applications. Plenty of handcrafted or learned features have been employed for a variety of classifiers and achieved promising performances for recognizing micro-expressions. However, the micro-expression recognition is still challenging due to the subtle spatiotemporal changes of micro-expressions. To exploit the merits of deep learning, we propose a novel deep recurrent convolutional networks based micro-expression recognition approach, capturing the spatial-temporal deformations of micro-expression sequence. Specifically, the proposed deep model is constituted of several recurrent convolutional layers for extracting visual features and a classificatory layer for recognition. It is optimized by an end-to-end manner and obviates manual feature design. To handle sequential data, we exploit two types of extending the connectivity of convolutional networks across temporal domain, in which the spatiotemporal deformations are modeled in views of facial appearance and geometry separately. Besides, to overcome the shortcomings of limited and imbalanced training samples, temporal data augmentation strategies as well as a balanced loss are jointly used for our deep network. By performing the experiments on three spontaneous microexpression datasets, we verify the effectiveness of our proposed micro-expression recognition approach compared to the state-ofthe-art methods.

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A Delaunay-Based Temporal Coding Model for Micro-expression Recognition

Cited by 28 publications

References 18 publications

Objective Micro-Facial Movement Detection Using FACS-Based Regions and Baseline Evaluation

Objective Micro-Facial Movement Detection Using FACS-Based Regions and Baseline Evaluation

SAMM: A Spontaneous Micro-Facial Movement Dataset

Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-Expressions

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