Comparison Analysis of Different Face Detecting Techniques

Singh, Neha; Brisilla, R. M.

doi:10.1109/i-pact52855.2021.9696583

Cited by 6 publications

(6 citation statements)

References 14 publications

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“…Face detection is the crucial step in detecting the face of the driver and extracting the facial features to evaluate the drowsy state of the driver [ 27 , 28 , 29 ]. The three most popular techniques for detecting the face below are available below to implement behavioral measures.…”

Section: Methodsmentioning

confidence: 99%

“…This technique quickly detects the object efficiently, but it cannot detect faces under occlusion. It also generates many false predictions during the face detection procedure [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

“…CNN is a deep learning-based algorithm with Dlib that helps detect the face from different angles. Dlib is more accurate than OpenCV Haar Cascade, but the computational cost is very high and unable to run in real-time conditions [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

“…Table 3 shows the comparative analysis of various face detection techniques. Five parameters are used to analyze the best technique for driver drowsiness detection [ 27 ]. Table 3 revealed that the MTCNN provides a better result than the OpenCV Haar Cascade and Dlib.…”

Section: Methodsmentioning

confidence: 99%

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System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures

Bajaj

Kumar

Kaushal

et al. 2023

Sensors

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The amount of road accidents caused by driver drowsiness is one of the world’s major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver’s body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection methods using only single measures that failed to produce good outcomes. Furthermore, these measures were only functional in certain conditions. This paper proposes a model that combines the two approaches, non-intrusive and intrusive, to detect driver drowsiness. Behavioral measures as a non-intrusive approach and sensor-based physiological measures as an intrusive approach are combined to detect driver drowsiness. The proposed hybrid model uses AI-based Multi-Task Cascaded Convolutional Neural Networks (MTCNN) as a behavioral measure to recognize the driver’s facial features, and the Galvanic Skin Response (GSR) sensor as a physiological measure to collect the skin conductance of the driver that helps to increase the overall accuracy. Furthermore, the model’s efficacy has been computed in a simulated environment. The outcome shows that the proposed hybrid model is capable of identifying the transition from awake to a drowsy state in the driver in all conditions with the efficacy of 91%.

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Section: Methodsmentioning

confidence: 99%

“…This technique quickly detects the object efficiently, but it cannot detect faces under occlusion. It also generates many false predictions during the face detection procedure [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures

Bajaj

Kumar

Kaushal

et al. 2023

Sensors

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show abstract

“…In this paper, face detection is performed using the Haar cascade classifier. This Haar cascade classifier has a fairly fast detection speed than several other methods [27], [28]. Because of the speed of execution has a significant effect on the overall measurement results.…”

Section: Face Detection and Roi Selectionmentioning

confidence: 98%

Implementation of 1D convolutional neural network for improvement remote photoplethysmography measurement

Firmansyah

Prabowo²,

Suheta³

et al. 2023

IJEECS

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<span lang="EN-US">Remote photoplethysmography (rPPG) for non-contact heart rate measurement has been widely developed and shows good development. However, motion artifact due to changes in illumination and subject movement is still the main problem. Especially when measurements are taken in real conditions. In these conditions, it will be vulnerable to rPPG signal readings with poor signal quality. So, in this paper, it is proposed to classify the signal quality using one dimensional convolutional neural network (1D CNN). The classification is carried out based on the extraction of the temporal features of the rPPG signal that has been obtained from the plane orthogonal to skin algorithm and the magnitude of the subject's movement when measured. The classification results are entered into a compensated network if the signal obtained shows moderate quality. The compensated network will provide a more accurate estimate of hr value. The test was carried out using a dataset of 10 subjects, each measured with 3 different types of illumination. In the experiments conducted, the system's performance showed an improvement compared to the POS algorithm alone. The experiment found that the mean absolute error measurement was 2.78, and the mean error was relative at 3.67%.</span>

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Design and Implementation of Automated Nasal Swab Robots by Integrating Visual Detection and Compliance Control

Hsiao,

Jhang,

Tung

et al. 2024

2024 International Conference on Advanced Robotics and Intelligent Systems (ARIS)

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Comparison Analysis of Different Face Detecting Techniques

Cited by 6 publications

References 14 publications

System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures

System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures

Implementation of 1D convolutional neural network for improvement remote photoplethysmography measurement

Design and Implementation of Automated Nasal Swab Robots by Integrating Visual Detection and Compliance Control

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