Exercise Fatigue Detection Algorithm Based on Video Image Information Extraction

Zhang, Fan; Wang, Feng

doi:10.1109/access.2020.3023648

Cited by 22 publications

(9 citation statements)

References 37 publications

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“…By using the CK+ image set the performance based on three CNNs (on left-eye, right-eye, and lip region) are 92.19%, 93.11%, and 95.86% respectively, whereas by using the JAFFE image set the performances are gained 90.207%, 91.042%, and 92.120% respectively. Our achievement is excellent as compared to other researchers ( Zhang & Wang, 2020 ; Hossain & Sanyal, 2012b ; Moghaddam, Akbarizadeh & Kaabi, 2019 ; Cheon & Kim, 2008 ; AlBdairi, Zhu & Mohammed, 2020 ). Moreover, our proposed synthesis method gained an excellent rate of higher accuracy, i.e.…”

Section: Experimental Evaluationsupporting

confidence: 59%

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Facial expression recognition based on active region of interest using deep learning and parallelism

Hossain¹,

Assiri²

2022

PeerJ Computer Science

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The automatic facial expression tracking method has become an emergent topic during the last few decades. It is a challenging problem that impacts many fields such as virtual reality, security surveillance, driver safety, homeland security, human-computer interaction, medical applications. A remarkable cost-efficiency can be achieved by considering some areas of a face. These areas are termed Active Regions of Interest (AROIs). This work proposes a facial expression recognition framework that investigates five types of facial expressions, namely neutral, happiness, fear, surprise, and disgust. Firstly, a pose estimation method is incorporated and to go along with an approach to rotate the face to achieve a normalized pose. Secondly, the whole face-image is segmented into four classes and eight regions. Thirdly, only four AROIs are identified from the segmented regions. The four AROIs are the nose-tip, right eye, left eye, and lips respectively. Fourthly, an info-image-data-mask database is maintained for classification and it is used to store records of images. This database is the mixture of all the images that are gained after introducing a ten-fold cross-validation technique using the Convolutional Neural Network. Correlations of variances and standard deviations are computed based on identified images. To minimize the required processing time in both training and testing the data set, a parallelism technique is introduced, in which each region of the AROIs is classified individually and all of them run in parallel. Fifthly, a decision-tree-level synthesis-based framework is proposed to coordinate the results of parallel classification, which helps to improve the recognition accuracy. Finally, experimentation on both independent and synthesis databases is voted for calculating the performance of the proposed technique. By incorporating the proposed synthesis method, we gain 94.499%, 95.439%, and 98.26% accuracy with the CK+ image sets and 92.463%, 93.318%, and 94.423% with the JAFFE image sets. The overall accuracy is 95.27% in recognition. We gain 2.8% higher accuracy by introducing a decision-level synthesis method. Moreover, with the incorporation of parallelism, processing time speeds up three times faster. This accuracy proves the robustness of the proposed scheme.

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Section: Experimental Evaluationsupporting

confidence: 59%

“…These lead us to find out the concept of active regions of interest (AROI) on a human face. Zhang & Wang (2020) proposed face recognition algorithms for fatigue detection by using video image information.…”

Section: Related Workmentioning

confidence: 99%

Facial expression recognition based on active region of interest using deep learning and parallelism

Hossain¹,

Assiri²

2022

PeerJ Computer Science

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“…Fatigue driving is one of the main causes of tra c accidents, and the number of tra c accidents caused by it is increasing year by year [3]. Fatigue driving has a strong concealability, and usually only the driver himself can detect it.…”

Section: Introductionmentioning

confidence: 99%

Driving Detection Based on the Multifeature Fusion

Yang¹,

Lu²

2022

Journal of Control Science and Engineering

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In order to solve the problems of facial feature localization and driver fatigue state identification methods in driving fatigue detection, a driving detection method based on the multifeature fusion was proposed. This method uses a supervised descent algorithm to simultaneously locate multiple facial features of drivers. On the basis of blink, yawn and nod judgment, multiple characteristic values of blink frequency, yawn frequency, and nod frequency of drivers were extracted to establish a fatigue detection sample database, and a naive Bayes classifier was constructed to judge fatigue. When the driver appears fatigue driving, warning information is given in time in order to prevent traffic accidents. The experimental results show that two sample videos were selected for testing. The accuracy rate of video sample 1 and video sample 2 was 94.74% and 95.00%, respectively. Conclusion. In the actual driving environment video test results, the discriminant average accuracy of a driver fatigue state reaches 94.87%, which has a good performance.

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“…A t-test was utilized to compare outcome measures pre-and post-ablation through an intraprocedural 18F-fluorodeoxyglucose positron emission tomography (PET) scan assessment before and after PET/contrast-enhanced guided microwave ablation [22]. A fatigue characteristic parameter optimization selection algorithm utilized the classification performance of an SVM as an evaluation criterion and applied the sequential forward floating selection algorithm as a search strategy [23]. The algorithm aimed to reach the optimal feature subset of fatigue motion by reducing the dimensionality of the domain set of fatigue feature parameters.…”

Section: Related Workmentioning

confidence: 99%

A New Hybrid Feature Selection Method Using T-test and Fitness Function

Abdulmohsin¹,

Wahab²,

Hossen³

2021

Computers, Materials &Amp; Continua

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Feature selection (FS) (or feature dimensional reduction, or feature optimization) is an essential process in pattern recognition and machine learning because of its enhanced classification speed and accuracy and reduced system complexity. FS reduces the number of features extracted in the feature extraction phase by reducing highly correlated features, retaining features with high information gain, and removing features with no weights in classification. In this work, an FS filter-type statistical method is designed and implemented, utilizing a t-test to decrease the convergence between feature subsets by calculating the quality of performance value (QoPV). The approach utilizes the well-designed fitness function to calculate the strength of recognition value (SoRV). The two values are used to rank all features according to the final weight (FW) calculated for each feature subset using a function that prioritizes feature subsets with high SoRV values. An FW is assigned to each feature subset, and those with FWs less than a predefined threshold are removed from the feature subset domain. Experiments are implemented on three datasets: Ryerson Audio-Visual Database of Emotional Speech and Song, Berlin, and Surrey Audio-Visual Expressed Emotion. The performance of the F-test and F-score FS methods are compared to those of the proposed method. Tests are also conducted on a system before and after deploying the FS methods. Results demonstrate the comparative efficiency of the proposed method. The complexity of the system is calculated based on the time overhead required before and after FS. Results show that the proposed method can reduce system complexity.

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Exercise Fatigue Detection Algorithm Based on Video Image Information Extraction

Cited by 22 publications

References 37 publications

Facial expression recognition based on active region of interest using deep learning and parallelism

Facial expression recognition based on active region of interest using deep learning and parallelism

Driving Detection Based on the Multifeature Fusion

A New Hybrid Feature Selection Method Using T-test and Fitness Function

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