Study on PPG Biometric Recognition Based on Multifeature Extraction and Naive Bayes Classifier

Yang, Junfeng; Huang, Yu-Wen; Zhang, Ruili; Huang, Fuxian; Meng, Qinggang; Feng, Shixin

doi:10.1155/2021/5597624

Cited by 22 publications

(17 citation statements)

References 34 publications

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“…erefore, on the basis of facial feature localization, blink frequency, yawn frequency, and nod frequency were selected as fatigue characteristic values. Firstly, the state judgment of blinking, yawning, and nodding was analyzed [17], taking blinking judgment as an example, as shown in Figure 4. e eye aspect ratio (e h /e w ) was selected as the condition for blinking judgment.…”

Section: Extraction Of Fatigue Eigenvaluesmentioning

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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Section: Extraction Of Fatigue Eigenvaluesmentioning

confidence: 99%

Driving Detection Based on the Multifeature Fusion

Yang¹,

Lu²

2022

Journal of Control Science and Engineering

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“…In future work, we aim to change the cascade model to improve the feature extraction technique for PPG biometric recognition. 98.66 [25] 98.65 Ours 98.82 MIMIC [22] 97.15 [25] 97.76 Ours 97.97…”

Section: Discussionmentioning

confidence: 99%

Multifeature Deep Cascaded Learning for PPG Biometric Recognition

Liu

Huang

et al. 2022

Scientific Programming

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Aiming at the problem that the traditional photoplethysmography (PPG) biometric recognition based on sparse representation is not robust to noise and intraclass variations when the sample size is small, we propose a PPG biometric recognition method based on multifeature deep cascaded sparse representation (MFDCSR). The method consists of multifeature signal coding and deep cascaded coding. The function of multifeature signal coding is to extract the shape, wavelet, and principal component analysis features of the PPG signal and to perform sparse representation. Deep cascaded coding is multilayer feature coding. Each layer combines multifeature signal coding with the result of the previous layer as input, and the output of each layer is the input of the next layer. The function of deep cascade coding is to learn the features of the PPG signal, layer by layer, and to output the category distribution vector of the PPG signal in the last layer. Experiments demonstrate that MFDCSR has better recognition performance than current methods for PPG biometric recognition.

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“…Naive Bayes algorithm is a kind of module classifier with known prior probability and class-conditional probability. Its basic idea is to calculate the probability that resource X belongs to class H [25]. In Bayesian classifier, it is necessary to build a probabilistic classifier based on modeling word features of different classes.…”

Section: Classification Of College English Teaching Informationmentioning

confidence: 99%

Construction of College English Teaching Information Resource Integration Model Based on Fuzzy Clustering Algorithm

Hua

2022

Mobile Information Systems

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The existing college English teaching information resource integration methods have problems of low recall rate, low accuracy rate, and long integration time. Therefore, this paper constructs a college English teaching information resource integration model based on fuzzy clustering algorithm. The ant colony algorithm is used to optimize the topic crawler, the optimized topic crawler is used to collect the college English teaching information resources in the mixed teaching mode, and the weight and naive Bayes algorithm are combined to classify the collected resources. According to the results of resource classification, fuzzy clustering algorithm is used to construct the integration model of college English teaching information resources. The experimental results show that the recall rate of the proposed model is more than 95%, the accuracy rate is more than 94%, and the average resource integration time is only 0.71 s, indicating that the model has relatively reliable performance.

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Study on PPG Biometric Recognition Based on Multifeature Extraction and Naive Bayes Classifier

Cited by 22 publications

References 34 publications

Driving Detection Based on the Multifeature Fusion

Driving Detection Based on the Multifeature Fusion

Multifeature Deep Cascaded Learning for PPG Biometric Recognition

Construction of College English Teaching Information Resource Integration Model Based on Fuzzy Clustering Algorithm

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