scholarly journals The vehicle driver safety prediction system

2021 ◽  
Author(s):  
Radosław Wróbel ◽  
Gustaw Sierzputowski ◽  
Piotr Haller ◽  
Veselin Mihaylov ◽  
Radostin Dimitrov
Keyword(s):  
Region Of Interest ◽  
Prediction System ◽  
Driver Safety ◽  
Step Process ◽  
Face Region ◽  
Falling Asleep ◽  
Road Crash ◽  
The Face ◽  
Safety Systems ◽  
Proprietary System

The article presents analysis of road crash accidents. It presents the evolution of safety systems, starting from a description of the curently used vehicle-based systems, with particular emphasis on the prediction of the driver falling asleep. The article also proposes a proprietary system of sleep prediction based on the face detection of drivers. The detection of facial landmarks is presented as a two-step process: an algorithm finds faces in general, and then needs to localize key facial structures within the face region of interest. The article presents the operation of the algorithm to detect driver falling asleep; method of detection and analysis.

scholarly journals Automatic Approach for Mask Detection Effective: For COVID-19

2021 ◽  
Author(s):  
Debajyoty Banik ◽  
Saksham Rawat Rawat ◽  
Aayush Thakur ◽  
Pritee Parwekar ◽  
Suresh Chandra Satapathy
Keyword(s):  
Region Of Interest ◽  
Face Mask ◽  
Working Environment ◽  
Public Places ◽  
Detection Model ◽  
Face Region ◽  
Sense Of Security ◽  
Improper Use ◽  
The Face ◽  
Dual Stage

Abstract The outbreak of Coronavirus Disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a Deep Learning-based system that can detect instances of improper use of face masks. A dual-stage Convolutional Neural Network (CNN)architecture is used in our system to recognie masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. This paper will automate the tasks of mask detection in public places when incorporated with CCTV cameras and will alert the system manager when a person without mask or wearing incorrect mask tries to enter. This paper includes multi face detection model which has the potential to target and identify a group of people whether they are wearing masks or not. We tried to collect various facial pictures and tried to identify the face Region of Interest (ROI), and then we separated it. Applying facial milestones, to permit the restriction the eyes, nose, mouth, and so. face was then completed and we tried to detect the presence of mask. To prepare a custom face cover locator, breaking our venture into two unmistakable stages was required, each with its own separate sub-steps. 1. Preparing: Here, stacking our face veil discovery dataset from plate, preparing a model on this dataset, and afterward serializing the face cover locator to circle was the focus. 2. Sending: Once the face veil identifier is prepared, the accompanying advance of stacking the cover finder, performing face recognition, and afterward characterizing each face as with veil or without veil, can be executed.

Real-Time Driver Fatigue Detection Based on Eye State Recognition

2013 ◽  
Vol 457-458 ◽  
pp. 944-952 ◽  
Author(s):  
Chao Sun ◽  
Jian Hua Li ◽  
Yang Song ◽  
Lai Jin
Keyword(s):  
Real Time ◽  
Traffic Accidents ◽  
Region Of Interest ◽  
Principal Component ◽  
Radial Symmetry ◽  
Driver Fatigue ◽  
Adaboost Algorithm ◽  
Face Region ◽  
The Face ◽  
Eye Closure

One of the important causes of traffic accidents is driver fatigue. In this paper, a new real-time non-intrusive method to detect driver fatigue is proposed. Firstly, face region is detected by AdaBoost algorithm because of its robustness. Then a region of interest of the eye is defined based on face geometry. In this region, eye pupil is precisely located by radial symmetry transform. With principal component analysis (PCA), three eigen spaces are trained to recognize eye states. Open, closed eye samples and other non-eye samples in the face region are used to get these eigen spaces. At last, PERCLOS and consecutive eye closure time are adopted to detect driver fatigue. Experiments with thirty two participants in realistic driving condition show the reliability and the robustness of our system.

Face detection in single and multiple images using different skin color models

2020 ◽  
Vol 13 ◽  
Author(s):  
Manpreet Kaur ◽  
Jasdev Bhatti ◽  
Mohit Kumar Kakkar ◽  
Arun Upmanyu
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Input Image ◽  
Morphological Operations ◽  
Reliability Model ◽  
Multiple Images ◽  
Face Region ◽  
Bounding Box ◽  
The Face ◽  
Precision And Accuracy

Introduction: Face Detection is used in many different steams like video conferencing, human-computer interface, in face detection, and in the database management of image. Therefore, the aim of our paper is to apply Red Green Blue ( Methods: The morphological operations are performed in the face region to a number of pixels as the proposed parameter to check either an input image contains face region or not. Canny edge detection is also used to show the boundaries of a candidate face region, in the end, the face can be shown detected by using bounding box around the face. Results: The reliability model has also been proposed for detecting the faces in single and multiple images. The results of the experiments reflect that the algorithm been proposed performs very well in each model for detecting the faces in single and multiple images and the reliability model provides the best fit by analyzing the precision and accuracy. Moreover Discussion: The calculated results show that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images. Also, the evaluated results by this paper provides the better testing strategies that helps to develop new techniques which leads to an increase in research effectiveness. Conclusion: The calculated value of all parameters is helpful for proving that the proposed algorithm has been performed very well in each model for detecting the face by using a bounding box around the face in single as well as multiple images. The precision and accuracy of all three models are analyzed through the reliability model. The comparison calculated in this paper reflects that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images.

Face Image Segmentation Using Color Information and Saliency Map

2011 ◽  
Vol 55-57 ◽  
pp. 77-81
Author(s):  
Hui Ming Huang ◽  
He Sheng Liu ◽  
Guo Ping Liu
Keyword(s):  
Image Segmentation ◽  
Human Skin ◽  
Skin Color ◽  
Saliency Map ◽  
Face Image ◽  
Color Information ◽  
Attention Model ◽  
Face Area ◽  
Face Region ◽  
The Face

In this paper, we proposed an efficient method to address the problem of color face image segmentation that is based on color information and saliency map. This method consists of three stages. At first, skin colored regions is detected using a Bayesian model of the human skin color. Then, we get a chroma chart that shows likelihoods of skin colors. This chroma chart is further segmented into skin region that satisfy the homogeneity property of the human skin. The third stage, visual attention model are employed to localize the face region according to the saliency map while the bottom-up approach utilizes both the intensity and color features maps from the test image. Experimental evaluation on test shows that the proposed method is capable of segmenting the face area quite effectively，at the same time, our methods shows good performance for subjects in both simple and complex backgrounds, as well as varying illumination conditions and skin color variances.

Advanced Driver Assistance Systems (ADAS): Who’s Driving What and What’s Driving Use?

2021 ◽  
Vol 65 (1) ◽  
pp. 1220-1224
Author(s):  
Daniel Palac ◽  
Iiona D. Scully ◽  
Rachel K. Jonas ◽  
John L. Campbell ◽  
Douglas Young ◽  
...  
Keyword(s):  
Group Differences ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Driver Safety ◽  
Advanced Driver Assistance Systems ◽  
Additional Group ◽  
Vehicle Technology ◽  
Assistance Systems ◽  
Vehicle Technologies ◽  
Safety Systems

The emergence of vehicle technologies that promote driver safety and convenience calls for investigation of the prevalence of driver assistance systems as well as of their use rates. A consumer driven understanding as to why certain vehicle technology is used remains largely unexplored. We examined drivers’ experience using 13 different advanced driver assistance systems (ADAS) and several reasons that may explain rates of use through a nationally-distributed survey. Our analysis focused on drivers’ levels of understanding and trust with their vehicle’s ADAS as well as drivers’ perceived ease, or difficulty, in using the systems. Respondents’ age and experience with Level 0 or Level 1 technologies revealed additional group differences, suggesting older drivers (55+), and those with only Level 0 systems as using ADAS more often. These data are interpreted using the Driver Behavior Questionnaire framework and offer a snapshot of the pervasiveness of certain driver safety systems.

scholarly journals State model based face mask detection

2018 ◽  
Vol 7 (2.22) ◽  
pp. 35
Author(s):  
Kavitha M ◽  
Mohamed Mansoor Roomi S ◽  
K Priya ◽  
Bavithra Devi K
Keyword(s):  
Face Mask ◽  
Search Space ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
State Model ◽  
Face Region ◽  
Automatic Teller Machine ◽  
Foreground Region ◽  
The Face ◽  
Surveillance Mechanism

The Automatic Teller Machine plays an important role in the modern economic society. ATM centers are located in remote central which are at high risk due to the increasing crime rate and robbery.These ATM centers assist with surveillance techniques to provide protection. Even after installing the surveillance mechanism, the robbers fool the security system by hiding their face using mask/helmet. Henceforth, an automatic mask detection algorithm is required to, alert when the ATM is at risk. In this work, the Gaussian Mixture Model (GMM) is applied for foreground detection to extract the regions of interest (ROI) i.e. Human being. Face region is acquired from the foreground region through  the torso partitioning and applying Viola-Jones algorithm in this search space. Parts of the face such as Eye pair, Nose, and Mouth are extracted and a state model is developed to detect  mask.  

scholarly journals ID: 1026 Reconstruction of the major defects of the tissue scalp

2017 ◽  
Vol 4 (S) ◽  
pp. 100
Author(s):  
M.I. Muradov ◽  
K.B. Mukhamedkerim ◽  
A. ABaiguzeva ◽  
K.E. Kazantaev ◽  
D.Zh. Koshkarbaev
Keyword(s):  
Donor Site ◽  
Silicone Implants ◽  
Fat Transfer ◽  
Autologous Fat ◽  
Autologous Fat Transfer ◽  
Definitive Evidence ◽  
Face Region ◽  
The Face ◽  
Volume Measurements ◽  
The Mean

Background: To provide quantitative objective data demonstrating the longevity and amount of volume augmentation in the fatty dystrophy of the facial tissue obtained with autologous lipofilling.   Methods: In our clinic had been operated 8 patients for last 2 years with fatty dystrophy of the facial tissue. A prospective analysis of all patients who underwent at our private practice and were followed up for at least 1,5 year. Surgery was performed under general anesthesia it is necessary for clear results tissue correction. We based on the literature has seen numerous clinical reports highlighting the benefits of autologous fat transfer for face from that areas, fat was collected from the abdomen (most frequently used donor site), hips, outer thighs (saddle-bags), internal knee or thigh, with quantitative volume measurements evaluating the amount of postoperative volume change.   Results: Twenty eight patients were included in the study. The mean follow-up time was 18 months. The mean amount of autologous fat injected into each face region was 10-70 mL. Hypercorrection was performed after 3 months and it was 20-50% of the initial injected fat volume. Overall, the mean absolute volume augmentation measured at their last (after 6 month) post operative visit was 10-25%. There was variability between patients in the volume amount and percentage that remained. The resorption process was observed in two patients after 6 month. We made correction with hyaluronic acid and silicone implants.   Conclusion: To our knowledge, this study is the first clinical quantification in our practise of autologous fat transfer and/or grafting in the literature that provides definitive evidence on the amount as well as the resultant longevity in the face. Autologous fat transfer to the face has definite long-term volume augmentation results. On average, approximately 25-35% of the injected volume remains at 18 months. However, some variability exists in the percentage of  volume that remains that may require a touch-up procedure.

scholarly journals Integarted Minimum Cost Sub-Block Matching Distance based Face Recognition using Internet of Things

2019 ◽  
Vol 8 (11) ◽  
pp. 117-122
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Minimum Cost ◽  
Recognition System ◽  
Block Matching ◽  
Face Region ◽  
Input Face ◽  
Face Recognition System ◽  
The Face ◽  
Fuzzy C Means Clustering

Now a days one of the critical factors that affects the recognition performance of any face recognition system is partial occlusion. The paper addresses face recognition in the presence of sunglasses and scarf occlusion. The face recognition approach that we proposed, detects the face region that is not occluded and then uses this region to obtain the face recognition. To segment the occluded and non-occluded parts, adaptive Fuzzy C-Means Clustering is used and for recognition Minimum Cost Sub-Block Matching Distance(MCSBMD) are used. The input face image is divided in to number of sub blocks and each block is checked if occlusion present or not and only from non-occluded blocks MWLBP features are extracted and are used for classification. Experiment results shows our method is giving promising results when compared to the other conventional techniques.

scholarly journals Liveness Measurements Using Optical Flow for Biometric Person Authentication

2012 ◽  
Vol 19 (2) ◽  
pp. 257-268 ◽  
Author(s):  
Maciej Smiatacz
Keyword(s):  
Optical Flow ◽  
Video Sequences ◽  
Support Vector ◽  
Biometric Identification ◽  
Behavioral Characteristics ◽  
Security Level ◽  
Identification Algorithm ◽  
Face Region ◽  
The Face ◽  
Consumer Devices

Liveness Measurements Using Optical Flow for Biometric Person Authentication Biometric identification systems, i.e. the systems that are able to recognize humans by analyzing their physiological or behavioral characteristics, have gained a lot of interest in recent years. They can be used to raise the security level in certain institutions or can be treated as a convenient replacement for PINs and passwords for regular users. Automatic face recognition is one of the most popular biometric technologies, widely used even by many low-end consumer devices such as netbooks. However, even the most accurate face identification algorithm would be useless if it could be cheated by presenting a photograph of a person instead of the real face. Therefore, the proper liveness measurement is extremely important. In this paper we present a method that differentiates between video sequences showing real persons and their photographs. First we calculate the optical flow of the face region using the Farnebäck algorithm. Then we convert the motion information into images and perform the initial data selection. Finally, we apply the Support Vector Machine to distinguish between real faces and photographs. The experimental results confirm that the proposed approach could be successfully applied in practice.

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