Driver Fatigue Detection Using Viola Jones and Principal Component Analysis

Fatima, Bahjat; Raza, Basit; Ziauddin, Sheikh; Safi, Ali-Farid; Ramzan, Huma

doi:10.1080/08839514.2020.1723875

Cited by 41 publications

(20 citation statements)

References 18 publications

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“…So, unlike biological neurons, they are randomly arranged (Lin et al) [59]. Figure (7) shows the ANN structure most approximately and frequently used. The ANN structure is explained as follows:…”

Section: Real-time Eyes Detectionmentioning

confidence: 99%

“…Moreover, it is essential to detect faces in many of these applications in the shortest possible time [4] [5]. Face detection is the first stage in identification of the object, for example, for recognition, orientation, modeling, verification and monitoring [6] [7]. Facial images or videos data containing different locations, dimension and backgrounds are often searched for or indexed [8] [9].…”

Section: Introductionmentioning

confidence: 99%

“…Eye closure, blinking, eyelid distance, yawning, head rotation, and gaze position, head rotation can all be used to determine the driver's condition. Figure 1: depicts a system that has been designed will alarm in the hypo-vigilance states including fatigue and distraction [4] [7]. In general, face detection algorithms are classified into two groups [14] : first, Feature-Based methods and the second, Learning-Based methods.…”

Section: Introductionmentioning

confidence: 99%

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A systematic review on Evaluation of Existing Face Eyes Detection Algorithms for Monitoring Systems

Sulaiman

Kocher²

2022

ACAD J NAWROZ UNIV

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Among the many issues facing the world, the issue of traffic accidents, many security facilities have developed research on fingerprints, palmistry biometrics and other biometrics. In modern technologies, facial and eye detection algorithms have been developed and identified. This paper includes comparisons of face and eye tracking methods, which are extremely useful for identifying individuals in a variety of situations. Facial biometrics are more stable due to their distinct features, which gives them an advantage over other biometrics such as palmistry and fingerprint. This work dealt with 74 studies of face and eye detection and a comparison of algorithms that were used in previous researches, where the Viola-jones algorithm was identified as the best facial algorithm in terms of accuracy up to 98% for different types of face databases because this algorithm depends on geometry of face and also able to detect all faces in the same image, Furthermore, it has been determined that SVM is the best eye detection algorithm due to the accuracy of its characteristics and the speed of execution in real time so, the highest accuracy achieved was 98% accurate .

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Section: Real-time Eyes Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A systematic review on Evaluation of Existing Face Eyes Detection Algorithms for Monitoring Systems

Sulaiman

Kocher²

2022

ACAD J NAWROZ UNIV

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“…In a real-time environment, it is important to detect and monitor driver behavior to save human lives. To resolve this problem, there were many automatic driver fatigue detection systems [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ] developed in past studies. Several computer vision-based applications were developed in the past to detect and predict driver fatigue.…”

Section: Study Backgroundmentioning

confidence: 99%

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Abbas

Alsheddy

2020

Sensors

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Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.

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“…For example, Paidi et al used the MATLAB cascade object detector to recognize blinking eyes' detection for driver drowsiness detection task [9]. Moreover, Fatima et al applied several handcrafted techniques for detecting driver fatigue, such as Viola Jones and principal component analysis [10]. Moujahid et al also proposed several CNN-based methods to tackle the same issue [11].…”

Section: Introductionmentioning

confidence: 99%

Real‐Time Human Ear Detection Based on the Joint of Yolo and RetinaFace

Quoc

Hoang

2021

Complexity

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Biometric traits gradually proved their importance in real-life applications, especially in identification field. Among the available biometric traits, the unique shape of the human ear has also received loads of attention from scientists through the years. Hence, numerous ear-based approaches have been proposed with promising performance. With these methods, plenty problems can be solve by the distinctiveness of ear features, such as recognizing human with mask or diagnose ear-related diseases. As a complete identification system requires an effective detector for real-time application, and the current richness and variety of ear detection algorithms are poor due to the small and complex shape of human ears. In this paper, we introduce a new human ear detection pipeline based on the YOLOv3 detector. A well-known face detector named RetinaFace is also added in the detection system to narrow the regions of interest and enhance the accuracy. The proposed method is evaluated on an unconstrained dataset, which shows its effectiveness.

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Driver Fatigue Detection Using Viola Jones and Principal Component Analysis

Cited by 41 publications

References 18 publications

A systematic review on Evaluation of Existing Face Eyes Detection Algorithms for Monitoring Systems

A systematic review on Evaluation of Existing Face Eyes Detection Algorithms for Monitoring Systems

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Real‐Time Human Ear Detection Based on the Joint of Yolo and RetinaFace

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