scholarly journals Deteksi Mata Mengantuk pada Pengemudi Mobil Menggunakan Metode Viola Jones

2019 ◽  
Vol 18 (2) ◽  
pp. 321-329
Author(s):  
Imanuddin Imanuddin ◽  
Fachrid Alhadi ◽  
Raza Oktafian ◽  
Ahmad Ihsan
Keyword(s):  
Programming Language ◽  
High Speed ◽  
Detection System ◽  
Digital Camera ◽  
Human Beings ◽  
Drowsiness Detection ◽  
Detection Systems ◽  
The Face ◽  
Detection Characteristics ◽  
Matlab Programming

Computer Vision is one of the branches of Image processing science that allows a combination of human beings, such as identifying an object like an eye and taking a decision. Many of the face detection systems use the Viola Jones method as an object detection method. The method of Viola Jones is known by having high speed and accuracy because it is useful to combine several concepts such as (Haar Features, Integral Image, AdaBoost, and Cascade Classifier) into a major method for detecting objects. The programming language used in this study uses the MATLAB programming language to facilitate the process of creating the system. The research aims to implement Viola Jones into a simple eye-sensing drowsiness system by utilizing the existing libraries in the MATLAB programming language. Once the system is completed, a system test is performed against the detected drowsiness detection characteristics. This eye drowsiness detection system aims to determine if the car rider is sleepy or not when driving with an input in the form of eye detection taken using a digital camera and then inserted into a language Programming GUI Matlab where the value is taken binary eyes, sleepy eyes and not sleepy that will be a reference that will be processed later so that it can produce the output of a warning sound to the rider of the sleepy car vehicle or not The sleepy automatically. The testing of the program gained an amount detected 7 eyes of 10 eyes by using BW 0255 level which is useful to accelerate a program to detect sleepy eyes.

scholarly journals ANALYSIS OF EXPERIENCE IN THE APPLICATION OF MISSILE LAUNCH DETECTION SYSTEMS

2021 ◽  
pp. 118-129
Author(s):  
A. Pinchuk ◽  
M. Garbuz ◽  
P. Zeleny ◽  
D. Harnets ◽  
D. Ivanov
Keyword(s):  
High Speed ◽  
Armed Forces ◽  
The United States ◽  
Armed Conflicts ◽  
Desert Storm ◽  
Detection Systems ◽  
The Face ◽  
Protected Object ◽  
Conducting Research ◽  
The Impact

Analysis of combat losses of aircraft in local armed conflicts in recent decades shows that most cases of aircraft hits are related to the impact of guided surface-to-air and air-to-air missiles equipped with homing warheads. The use of modern guided missiles equipped with homing warheads is one of the main threats to aircraft of various types. This is due to the fact that modern guided missiles are characterized by high speed, maneuverability, accuracy of aiming and difficulty of detection. Solving the problem of protecting aircraft from guided missiles consists of several stages: detection of missile launch; confirmation that the detected missile is heading directly toward the protected object; missile identification and decision-making on the most effective countermeasure system employment. At present, there are no missile launch detection systems that guarantee a 100% probability of threat detection, but an analysis of aviation combat losses in local armed conflicts in recent decades convincingly shows that the number of combat losses of aircraft equipped with such systems is much lower than those in which missile launch detection is carried out visually. For example, most of the Soviet Union's losses during the war in Afghanistan and the United States‟ losses during Operation “Desert Storm” in Iraq were related to the use of portable anti-aircraft missile systems, which missiles were equipped with infrared homing warheads. Realizing the scale of the threat posed by such missiles, most of the world's leading countries have significantly increased the expenses on development new or improvement existing countermeasures. As a result, the aggregate losses of coalition forces in Iraq, Afghanistan and Syria since 2001 clearly suggest that these costs have paid off, with losses from the use of portable anti-aircraft missile systems significantly reduced, while the total number of combat sorties increased. Therefore, in the face of all the challenges and threats posed to Ukraine due to the aggressive actions of the Russian Federation, conducting research in the interests of aviation of the Armed Forces of Ukraine to improve the effectiveness of missile detection systems for ensuring timely detection of threats, warning of aircraft crew and activation in the automatic mode the means of countermeasures is as relevant as ever.

scholarly journals VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Hisham A. Kholidy ◽  
Abdelkarim Erradi
Keyword(s):  
Power Systems ◽  
Large Volume ◽  
High Speed ◽  
Detection System ◽  
Heterogeneous Data ◽  
Area Measurement ◽  
Volume Data ◽  
Detection Systems ◽  
Key Events ◽  
Reduction Approach

The Cypher Physical Power Systems (CPPS) became vital targets for intruders because of the large volume of high speed heterogeneous data provided from the Wide Area Measurement Systems (WAMS). The Nonnested Generalized Exemplars (NNGE) algorithm is one of the most accurate classification techniques that can work with such data of CPPS. However, NNGE algorithm tends to produce rules that test a large number of input features. This poses some problems for the large volume data and hinders the scalability of any detection system. In this paper, we introduce VHDRA, a Vertical and Horizontal Data Reduction Approach, to improve the classification accuracy and speed of the NNGE algorithm and reduce the computational resource consumption. VHDRA provides the following functionalities: (1) it vertically reduces the dataset features by selecting the most significant features and by reducing the NNGE’s hyperrectangles. (2) It horizontally reduces the size of data while preserving original key events and patterns within the datasets using an approach called STEM, State Tracking and Extraction Method. The experiments show that the overall performance of VHDRA using both the vertical and the horizontal reduction reduces the NNGE hyperrectangles by 29.06%, 37.34%, and 26.76% and improves the accuracy of the NNGE by 8.57%, 4.19%, and 3.78% using the Multi-, Binary, and Triple class datasets, respectively.

scholarly journals Driver Drowsiness Detection System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 2270-2272
Author(s):  
Charan M
Keyword(s):  
Real Time ◽  
Web Application ◽  
Detection System ◽  
Smart Phone ◽  
Learning Approaches ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
The Face ◽  
Bounding Boxes ◽  
Traditional Image

We propose a Driver drowsiness detection system, the purposes of which are to prevent from dangerous cause and to avoid accidents. Since all the processes on image recognition performed on a smart phone, the system does not need to send images to a server and runs on an android smart phone in a real-time way. Automatic image-based recognition is a particularly challenging task. Traditional image analysis approaches have achieved low classification accuracy in the past, whereas deep learning approaches without human supervision real-time drowsiness detection. This model classifies whether the person’s eyes are opened or closed. To recognize the face, a user should have to adjust camera such a way that it covers his face first, and then the system starts recognition within the indicated bounding boxes. In addition, the system estimates the actions of the person. This recognition process is performed repeatedly about every second. We will implement this system as Web application effectively for real-time recognition.

scholarly journals Viewpoint Robustness of Automated Facial Action Unit Detection Systems

2021 ◽  
Vol 11 (23) ◽  
pp. 11171
Author(s):  
Shushi Namba ◽  
Wataru Sato ◽  
Sakiko Yoshikawa
Keyword(s):  
Prediction Accuracy ◽  
Detection System ◽  
Action Unit ◽  
Action Detection ◽  
Facial Action ◽  
Detection Systems ◽  
The Face ◽  
Overall Performance ◽  
Action Unit Detection ◽  
Facial Images

Automatic facial action detection is important, but no previous studies have evaluated pre-trained models on the accuracy of facial action detection as the angle of the face changes from frontal to profile. Using static facial images obtained at various angles (0°, 15°, 30°, and 45°), we investigated the performance of three automated facial action detection systems (FaceReader, OpenFace, and Py-feat). The overall performance was best for OpenFace, followed by FaceReader and Py-Feat. The performance of FaceReader significantly decreased at 45° compared to that at other angles, while the performance of Py-Feat did not differ among the four angles. The performance of OpenFace decreased as the target face turned sideways. Prediction accuracy and robustness to angle changes varied with the target facial components and action detection system.

scholarly journals Neural Network Based Detection of Driver’s Drowsiness

2021 ◽  
Author(s):  
Shraddha Bhandarkar ◽  
Tanvi Naxane ◽  
Sayli Shrungare ◽  
Shivani Rajhance
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Traditional Methods ◽  
Drowsiness Detection ◽  
Proposed Model ◽  
Falling Asleep ◽  
Image Dataset ◽  
Behavioral Aspects ◽  
The Face ◽  
Image Templates

<div>The primary purpose of this paper was to propose a way to alert sleepy drivers in the act of driving. Most of the traditional methods to detect drowsiness are based on behavioral aspects while some are intrusive and may distract drivers, while some require expensive sensors/hardware. Therefore, in this paper, driver’s drowsiness detection system is developed and implemented to aid drowsy drivers from falling asleep and to prevent accidents. The system takes images from the device as input. Using these image templates, the trained model starts execution and predicts/classifies whether the face of the person in the image is drowsy or alert. The proposed model is able to achieve accuracy of 99.93% using CNN on trained image dataset.</div>

scholarly journals Security Implementation by Machine Learning Based Face Recognization with SVM

2021 ◽  
pp. 214-218
Author(s):  
Apurva Yawalikar ◽  
U. W. Hore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Detection System ◽  
Object Class ◽  
Detection Systems ◽  
The Face ◽  
Object Class Detection ◽  
Human Faces ◽  
Work Done

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

Intrusion Detection Systems Alerts Reduction

2018 ◽  
pp. 255-275 ◽  
Author(s):  
Aymen Akremi ◽  
Hassen Sallay ◽  
Mohsen Rouached
Keyword(s):  
Intrusion Detection ◽  
High Speed ◽  
Detection System ◽  
Search Space ◽  
Relevant Information ◽  
Intrusion Detection Systems ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Rules Set ◽  
Intrusion Alerts

Investigators search usually for any kind of events related directly to an investigation case to both limit the search space and propose new hypotheses about the suspect. Intrusion detection system (IDS) provide relevant information to the forensics experts since it detects the attacks and gathers automatically several pertinent features of the network in the attack moment. Thus, IDS should be very effective in term of detection accuracy of new unknown attacks signatures, and without generating huge number of false alerts in high speed networks. This tradeoff between keeping high detection accuracy without generating false alerts is today a big challenge. As an effort to deal with false alerts generation, the authors propose new intrusion alert classifier, named Alert Miner (AM), to classify efficiently in near real-time the intrusion alerts in HSN. AM uses an outlier detection technique based on an adaptive deduced association rules set to classify the alerts automatically and without human assistance.

scholarly journals Face Detection and Processing: a Survey

2018 ◽  
Vol 7 (4.19) ◽  
pp. 1066
Author(s):  
R. P.Dahake ◽  
M. U. Kharat
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Detection System ◽  
Research Work ◽  
Processing Unit ◽  
Detection Systems ◽  
Haar Feature ◽  
The Face ◽  
Feature Based ◽  
Graphical Processing

In the recent era facial image processing is gaining more importance and the face detection from image or from video have  number of applications  which are video surveillance, entertainment, security, multimedia, communication, Ubiquitous computing etc. Various research work are carried out for  face detection and processing which includes detection, tracking of the face, estimation of pose, clustering the detected faces etc. Although significant advances have been made, the performance of face detection systems provide satisfactory under controlled environment & may get degraded with some challenging scenario such as in real time video face detection and processing. There are many real-time applications where human face serves as identity and these application are time bound so time for detection of face from image or video and the further processing is very essential, thus here our goal is to discuss the face detection system overview and to review various human skin colors based approaches and Haar feature based approach for better detection performance. Detected faces tagging and clustering is essential in some cases, so for such further processing time factor plays important role. Some of the recent approaches to improve detection speed such as using Graphical Processing Unit are discussed and providing future directions in this area. 

Face Detection Method Based on SSR Algorithm

2011 ◽  
Vol 128-129 ◽  
pp. 130-133
Author(s):  
Kai Song ◽  
Lei Wang ◽  
Zhi Kun Liu
Keyword(s):  
Face Detection ◽  
Template Matching ◽  
High Speed ◽  
Detection System ◽  
High Reliability ◽  
Accuracy Rate ◽  
Level Information ◽  
The Face ◽  
Feature Based ◽  
Location Detection

In order to achieve high speed and high reliability of face detection system, a feature-based template-matching algorithm to detect a key part between the two eyes used. The method is achieved through divided a filter into six rectangular. The face detection process adopts the “integral image” theory. Just calculating the information of around the eyes, eyebrows, and nose area, aspect ratio of the value of the SSR filter is set to 2.22:1 through the experimental which improved the SSR algorithm. Using the advantages of gray-level information, it has better reliability on the changes in light conditions. The experiment result shows that the accuracy rate of the standard frontal face detection is 95%; to monitoring images, the accuracy rate of the “eyes - human face” window location detection is 90%

Face Spoof Detection System Based on Genetic Algorithm and Artificial Intelligence Technique: A Review

2018 ◽  
Vol 8 (6) ◽  
pp. 51
Author(s):  
Diksha Anand ◽  
Kamal Gupta
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Replay Attack ◽  
Detection Systems ◽  
System A ◽  
The Face ◽  
Alternative Means ◽  
Authentication System ◽  
Real Person ◽  
Face Spoofing

Face recognition is an alternative means to authenticate a person in different applications for access control. Instead of many improvements, this method is prone to various attacks like photos, 3D masks and video replay attack. Due to these attacks, system should require a face spoof detection system. A face spoof detection systems have an ability to identify whether a face is from a real person or a fake image. Face spoofing effect the image by adding deformation in it and also degrades the image pattern quality. Face spoofing detection system automatically identifies the human face is a true face or a fake face. In today's era, face recognition method is widely used to authenticate the face (like for unlocking mobile phones etc.) and providing access to the services or facilities but some intruders use various trick to crack the authentication system by presenting the false face in front of the authentication system, so it become necessity to prevent our face authentication system from face spoofing attack. So the choice of the technique to detect the face spoofing attack should be accurate and highly efficient.

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