Facial Action Unit Detection with Multilayer Fused Multi-Task and Multi-Label Deep Learning Network

doi:10.3837/tiis.2019.11.015

Cited by 4 publications

(2 citation statements)

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“…The deep learning model presented in [20] [21] fuses the multi-modal information with heterogenous signals to improve the stress detection performance. The collected video data, respiration, and ECG data are preprocessed and fed into a deep neural network along with facial feature sequences.…”

Section: Deep Learning Based Emotion Classification Modelsmentioning

confidence: 99%

Stress Level Based Emotion Classification Using Hybrid Deep Learning Algorithm

2023

KSII TIIS

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The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

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Section: Deep Learning Based Emotion Classification Modelsmentioning

confidence: 99%

Stress Level Based Emotion Classification Using Hybrid Deep Learning Algorithm

2023

KSII TIIS

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“…Emotion refers to a conscious mental reaction subjectively experienced as strong feeling typically accompanied by physiological and behavioral changes in the body [3]. To recognize a user's emotional state, several studies have applied different forms of input, such as speech, facial expression, video, text, and others [11,13,15,25,39,42,47]. Among the methods using these inputs, facial emotion recognition (FER) has been gaining substantial attention over the past decades.…”

Section: Introductionmentioning

confidence: 99%

Human emotion recognition based on the weighted integration method using image sequences and acoustic features

Byun

Lee

2020

Multimed Tools Appl

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People generally perceive other people’s emotions based on speech and facial expressions, so it can be helpful to use speech signals and facial images simultaneously. However, because the characteristics of speech and image data are different, combining the two inputs is still a challenging issue in the area of emotion-recognition research. In this paper, we propose a method to recognize emotions by synchronizing speech signals and image sequences. We design three deep networks. One of the networks is trained using image sequences, which focus on facial expression changes. Facial landmarks are also input to another network to reflect facial motion. The speech signals are first converted to acoustic features, which are used for the input of the other network, synchronizing the image sequence. These three networks are combined using a novel integration method to boost the performance of emotion recognition. A test comparing accuracy is conducted to verify the proposed method. The results demonstrated that the proposed method exhibits more accurate performance than previous studies.

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