Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification

Rodríguez, Paul; Cucurull, Guillem; Gonzàlez, Jordi; Gonfaus, Josep M.; Nasrollahi, Kamal; Moeslund, Thomas B.; Roca, Francesc

doi:10.1109/tcyb.2017.2662199

Cited by 214 publications

(166 citation statements)

References 63 publications

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“…The latter layers of the convoluted neural network are typically a number of fully connected layers and a classifier. In recent years, convolution neural networks have been successfully applied to facial expression recognition [30, 31], face recognition [32, 33], human posture estimation [34], age estimation [35, 36], and speech recognition [37, 38]. …”

Section: Methodsmentioning

confidence: 99%

Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image

Huan

Wen

Zhang

et al. 2017

Computational and Mathematical Methods in Medicine

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Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.

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Section: Methodsmentioning

confidence: 99%

Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image

Huan

Wen

Zhang

et al. 2017

Computational and Mathematical Methods in Medicine

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“…The landmark-based approaches quantify facial movements using key facial points applying the sequence of face detection 25,115 and facial landmarks estimation. 117,118 These proposals have demonstrated that the most important advantage of deep learning is that the learned dynamic appearance or discriminative features of facial expressions could be generalized and applied to different datasets and applications. On the other hand, the region-based methods process the raw sequence of images through an end-to-end deep learning model to learn the spatiotemporal features.…”

Section: Human Motion Analysis and Deep Learningmentioning

confidence: 99%

“…On the other hand, the region-based methods process the raw sequence of images through an end-to-end deep learning model to learn the spatiotemporal features. 117,118 These proposals have demonstrated that the most important advantage of deep learning is that the learned dynamic appearance or discriminative features of facial expressions could be generalized and applied to different datasets and applications.…”

Section: Human Motion Analysis and Deep Learningmentioning

confidence: 99%

Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey

et al. 2017

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Epilepsy being one of the most prevalent neurological disorders, affecting approximately 50 million people worldwide, and with almost 30-40% of patients experiencing partial epilepsy being nonresponsive to medication, epilepsy surgery is widely accepted as an effective therapeutic option. Presurgical evaluation has advanced significantly using noninvasive techniques based on video monitoring, neuroimaging, and electrophysiological and neuropsychological tests; however, certain clinical settings call for invasive intracranial recordings such as stereoelectroencephalography (SEEG), aiming to accurately map the eloquent brain networks involved during a seizure. Most of the current presurgical evaluation procedures focus on semiautomatic techniques, where surgery diagnosis relies immensely on neurologists' experience and their time-consuming subjective interpretation of semiology or the manifestations of epilepsy and their correlation with the brain's electrical activity. Because surgery misdiagnosis reaches a rate of 30%, and more than one-third of all epilepsies are poorly understood, there is an evident keen interest in improving diagnostic precision using computer-based methodologies that in the past few years have shown near-human performance. Among them, deep learning has excelled in many biological and medical applications, but has advanced insufficiently in epilepsy evaluation and automated understanding of neural bases of semiology. In this paper, we systematically review the automatic applications in epilepsy for human motion analysis, brain electrical activity, and the anatomoelectroclinical correlation to attribute anatomical localization of the epileptogenic network to distinctive epilepsy patterns. Notably, recent advances in deep learning techniques will be investigated in the contexts of epilepsy to address the challenges exhibited by traditional machine learning techniques. Finally, we discuss and propose future research on epilepsy surgery assessment that can jointly learn across visually observed semiologic patterns and recorded brain electrical activity.

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“…The prediction for each of these modalities was obtained using binary classifiers (e.g., SVM) trained using handcrafted features. Recently, deep learning methods [15], [16] have become popular in pain classification. In case of infants, Celona and Manoni [17] proposed a framework that combines both handcrafted and deep features for classifying COPE images as pain/no-pain.…”

Section: Introductionmentioning

confidence: 99%

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

Salekin

Goldgof

Kasturi

et al. 2019

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

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Neonates do not have the ability to either articulate pain or communicate it non-verbally by pointing. The current clinical standard for assessing neonatal pain is intermittent and highly subjective. This discontinuity and subjectivity can lead to inconsistent assessment, and therefore, inadequate treatment. In this paper, we propose a multi-channel deep learning framework for assessing neonatal pain from videos. The proposed framework integrates information from two pain indicators or channels, namely facial expression and body movement, using convolutional neural network (CNN). It also integrates temporal information using a recurrent neural network (LSTM). The experimental results prove the efficiency and superiority of the proposed temporal and multi-channel framework as compared to existing similar methods.

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Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification

Abstract: This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

Cited by 214 publications

References 63 publications

Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image

Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image

Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

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