Dynamic Facial Stress Recognition in Temporal Convolutional Network

Feng, Sidong

doi:10.1007/978-3-030-36808-1_76

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Another interesting work in which images were used can be found in Ref. 115 In this work, they used a TCN model to dynamically detect stress through facial photographs. 3.…”

Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyperparameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.

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“…Another interesting work in which images were used can be found in Ref. 115 In this work, they used a TCN model to dynamically detect stress through facial photographs. 3.…”

Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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“…In the method using NIR images, stress recognition was performed using an SVM after extracting scale-invariant feature transform (SIFT) descriptors around facial landmarks. In other studies, stress was recognized by fusing RGB and thermal images [28,39,40]. In these methods, stress was recognized using the features extracted from super-pixels and local binary patterns on the three orthogonal plane (LBP-TOP) descriptor.…”

Section: Facial-image-based Stress Recognition Methodsmentioning

confidence: 99%

“…However, in many of these methods, handcrafted features continue to be used. In some recent studies, a neural network with handcrafted features is used in the feature extraction process [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information

Jeon

Bae

Lee

et al. 2021

Sensors

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In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features. However, such features have low discriminating power. We propose a deep-learning-based stress-recognition method using facial images to address these challenges. Given that deep-learning methods require extensive data, we constructed a large-capacity image database for stress recognition. Furthermore, we used temporal attention, which assigns a high weight to frames that are highly related to stress, as well as spatial attention, which assigns a high weight to regions that are highly related to stress. By adding a network that inputs the facial landmark information closely related to stress, we supplemented the network that receives only facial images as the input. Experimental results on our newly constructed database indicated that the proposed method outperforms contemporary deep-learning-based recognition methods.

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“…It is useful for detection of activity using skeletal joint movements [51], classification of stress [52], and early predictions [53].…”

Section: It Work Best For Time-seriesbased Datamentioning

confidence: 99%

Counter-Epidemiological Projections of e-Coaching

Lai¹,

Yanushkevich²,

Shmerko³

2021

Preprint

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This paper considers e-coaching at times of pandemic. It utilizes the Emergency Management Cycle (EMC), a core doctrine for managing disasters. The EMC dimensions provide a useful taxonomical view for the development and application of e-coaching systems, emphasizing technological and societal issues. Typical pandemic symptoms such as anxiety, panic, avoidance, and stress, if properly detected, can be mitigated using the e-coaching tactic and strategy. In this work, we focus on a stress monitoring assistant developed upon machine learning techniques. We provide the results of an experimental study of a prototype of such an assistant. Our study leads to the conclusion that stress monitoring shall become a valuable component of e-coaching at all EMC phases.

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Dynamic Facial Stress Recognition in Temporal Convolutional Network

Cited by 5 publications

References 15 publications

Deep Learning for Time Series Forecasting: A Survey

Deep Learning for Time Series Forecasting: A Survey

Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information

Counter-Epidemiological Projections of e-Coaching

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