Pupil Detection in Facial Images with Using Bag of Pixels

2012 ◽  
Vol 468-471 ◽  
pp. 2941-2948
Author(s):  
Mohammad Ali Azimi Sotudeh ◽  
Hasan Ziafat ◽  
Said Ghafari
Keyword(s):  
Eye Tracking ◽  
Detection System ◽  
Horizontal Projection ◽  
Distinctive Features ◽  
Region Detection ◽  
Face Region ◽  
Pupil Detection ◽  
Intersection Method ◽  
Facial Images ◽  
Eye Region

To detect and track eye images, distinctive features of user eye are used. Generally, an eye-tracking and detection system can be divided into four steps: Face detection, eye region detection, pupil detection and eye tracking. To find the position of pupil, first, face region must be separated from the rest of the image using bag of pixels, this will cause the images background to be non effective in our next steps. We used from horizontal projection, to separate a region containing eyes and eyebrow. This will result in decreasing the computational complexity and ignoring some factors such as bread. Finally, in proposed method points with the highest values of are selected as the eye candidate's. The eye region is well detected among these points. Color entropy in the eye region is used to eliminate the irrelevant candidates. With a pixel of the iris or pupil can be achieved center of pupil. To find the center of pupil can be used line intersection method in the next step, we perform eye tracking. The proposed method achieve a correct eye detection rate of 97.3% on testing set that gathered from different images of face data. Moreover, in the case of glasses the performance is still acceptable.

scholarly journals A Driver Face Monitoring System for Fatigue and Distraction Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Mohamad-Hoseyn Sigari ◽  
Mahmood Fathy ◽  
Mohsen Soryani
Keyword(s):  
Monitoring System ◽  
Template Matching ◽  
Head Rotation ◽  
Driver Fatigue ◽  
Horizontal Projection ◽  
Closure Rate ◽  
Real Time System ◽  
Face Region ◽  
Eye Closure ◽  
Eye Region

Driver face monitoring system is a real-time system that can detect driver fatigue and distraction using machine vision approaches. In this paper, a new approach is introduced for driver hypovigilance (fatigue and distraction) detection based on the symptoms related to face and eye regions. In this method, face template matching and horizontal projection of top-half segment of face image are used to extract hypovigilance symptoms from face and eye, respectively. Head rotation is a symptom to detect distraction that is extracted from face region. The extracted symptoms from eye region are (1) percentage of eye closure, (2) eyelid distance changes with respect to the normal eyelid distance, and (3) eye closure rate. The first and second symptoms related to eye region are used for fatigue detection; the last one is used for distraction detection. In the proposed system, a fuzzy expert system combines the symptoms to estimate level of driver hypo-vigilance. There are three main contributions in the introduced method: (1) simple and efficient head rotation detection based on face template matching, (2) adaptive symptom extraction from eye region without explicit eye detection, and (3) normalizing and personalizing the extracted symptoms using a short training phase. These three contributions lead to develop an adaptive driver eye/face monitoring. Experiments show that the proposed system is relatively efficient for estimating the driver fatigue and distraction.

scholarly journals Techniques Used for Eye Gaze Interfaces and Survey

2015 ◽  
Vol 1 (6) ◽  
pp. 276
Author(s):  
Maria Rashid ◽  
Wardah Mehmood ◽  
Aliya Ashraf
Keyword(s):  
Eye Tracking ◽  
Eye Gaze ◽  
3D Models ◽  
Image Process ◽  
Usability Problems ◽  
Software Algorithms ◽  
Pupil Detection ◽  
Eye Movement Tracking ◽  
2D And 3D ◽  
Movement Tracking

Eye movement tracking is a method that is now-a-days used for checking the usability problems in the contexts of Human Computer Interaction (HCI). Firstly we present eye tracking technology and key elements.We tend to evaluate the behavior of the use when they are using the interace of eye gaze. Used different techniques i.e. electro-oculography, infrared oculography, video oculography, image process techniques, scrolling techniques, different models, probable approaches i.e. shape based approach, appearance based methods, 2D and 3D models based approach and different software algorithms for pupil detection etc. We have tried to compare the surveys based on their geometric properties and reportable accuracies and eventually we conclude this study by giving some prediction regarding future eye-gaze. We point out some techniques by using various eyes properties comprising nature, appearance and gesture or some combination for eye tracking and detection. Result displays eye-gaze technique is faster and better approach for selection than a mouse selection. Rate of error for all the matters determines that there have been no errors once choosing from main menus with eye mark and with mouse. But there have been a chance of errors when once choosing from sub menus in case of eye mark. So, maintain head constantly in front of eye gaze monitor.

scholarly journals ADOS-Eye-Tracking: The Archimedean Point of View and Its Absence in Autism Spectrum Conditions

2021 ◽  
Vol 12 ◽  
Author(s):  
Ulrich Max Schaller ◽  
Monica Biscaldi ◽  
Anna Burkhardt ◽  
Christian Fleischhaker ◽  
Michael Herbert ◽  
...  
Keyword(s):  
Social Interaction ◽  
Eye Tracking ◽  
Children And Adolescents ◽  
Face Perception ◽  
Autism Spectrum ◽  
Autism Spectrum Conditions ◽  
Gaze Behavior ◽  
Face Region ◽  
Mobile Eye Tracking ◽  
Standardized Diagnostic

Face perception and emotion categorization are widely investigated under laboratory conditions that are devoid of real social interaction. Using mobile eye-tracking glasses in a standardized diagnostic setting while applying the Autism Diagnostic Observation Schedule (ADOS-2), we had the opportunity to record gaze behavior of children and adolescents with and without Autism Spectrum Conditions (ASCs) during social interaction. The objective was to investigate differences in eye-gaze behavior between three groups of children and adolescents either (1) with ASC or (2) with unconfirmed diagnosis of ASC or (3) with neurotypical development (NTD) during social interaction with an adult interviewer in a diagnostic standard situation using the ADOS-2. In a case control study, we used mobile eye-tracking glasses in an ecologically valid and highly standardized diagnostic interview to investigate suspected cases of ASC. After completion of the ASC diagnostic gold standard including the ADOS-2, the participants were assigned to two groups based on their diagnosis (ASC vs. non-ASC) and compared with a matched group of neurotypically developed controls. The primary outcome measure is the percentage of total dwell times assessed for different areas of interest (AOI) with regard to the face and body of a diagnostic interviewer and the surrounding space. Overall, 65 children and adolescents within an age range of 8.3–17.9 years were included in the study. The data revealed significant group differences, especially in the central-face area. Previous investigations under laboratory conditions gave preferential attention to the eye region during face perception to describe differences between ASC and NTD. In this study – using an ecologically valid setting within a standard diagnostic procedure – the results indicate that neurotypically developed controls seem to process faces and facial expressions in a holistic manner originating from the central-face region. Conversely, participants on the Autism Spectrum (tAS) seem to avoid the central-face region and show unsystematic gaze behavior, not using the preferred landing position in the central-face region as the Archimedean point of face perception. This study uses a new approach, and it will be important to replicate these preliminary findings in future research.

Human Face Region Detection Driving Activity Recognition in Video

2018 ◽  
pp. 2102-2123
Author(s):  
Anastasios Doulamis ◽  
Athanasios Voulodimos ◽  
Theodora Varvarigou
Keyword(s):  
Activity Recognition ◽  
Human Face ◽  
Multiple Streams ◽  
Body Movements ◽  
Video Signals ◽  
Region Detection ◽  
Face Region ◽  
Classification Framework ◽  
Fusion Methods ◽  
Tremendous Impact

Automatic recognition of human actions from video signals is probably one of the most salient research topics of computer vision with a tremendous impact for many applications. In this chapter, the authors introduce a new descriptor, the Human Constrained Pixel Change History (HC-PCH), which is based on PCH but focuses on the human body movements over time. They propose a modification of the conventional PCH that entails the calculation of two probabilistic maps based on human face and body detection, respectively. These HC-PCH features are used as input to an HMM-based classification framework, which exploits redundant information from multiple streams by employing sophisticated fusion methods, resulting in enhanced activity recognition rates.

A Survey on Intrusion Detection System for Software Defined Networks (SDN)

2021 ◽  
pp. 467-489
Author(s):  
Yogita Hande ◽  
Akkalashmi Muddana
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Network Architecture ◽  
Detection System ◽  
Learning Approach ◽  
Distinctive Features ◽  
Detection Systems ◽  
Security Challenges ◽  
Deep Learning Model

Presently, the advances of the internet towards a wide-spread growth and the static nature of traditional networks has limited capacity to cope with organizational business needs. The new network architecture software defined networking (SDN) appeared to address these challenges and provides distinctive features. However, these programmable and centralized approaches of SDN face new security challenges which demand innovative security mechanisms like intrusion detection systems (IDS's). The IDS of SDN are designed currently with a machine learning approach; however, a deep learning approach is also being explored to achieve better efficiency and accuracy. In this article, an overview of the SDN with its security concern and IDS as a security solution is explained. A survey of existing security solutions designed to secure the SDN, and a comparative study of various IDS approaches based on a deep learning model and machine learning methods are discussed in the article. Finally, we describe future directions for SDN security.

Color Images Illumination Compensation Method for Human Face Region Detection

2014 ◽  
Vol 678 ◽  
pp. 162-165 ◽  
Author(s):  
Yang Yu ◽  
Xiao Bin Li ◽  
Hai Yan Sun
Keyword(s):  
Color Images ◽  
Compensation Method ◽  
Detection Accuracy ◽  
Human Face ◽  
Light Conditions ◽  
Illumination Compensation ◽  
Facial Region ◽  
Region Detection ◽  
Face Region ◽  
California Institute Of Technology

Facial region detection has broad application prospects, but existing human face region detection methods have some rigorous requirements for the light conditions. Error detection about face area caused by the poor light conditions has a great bad effect on the follow-up processing, such as face recognition, fatigue degree evaluation based on visual. So face region detection in complex lighting conditions has always been the difficult problem. Therefore a self-adaptive illumination compensation method for color images has been proposed. Select the color face images database of the California Institute of Technology to test the method dealing with the face region detection by original image and the image after illumination compensation. In the simulation experiment, the method of Illumination compensation can effectively improve the detection accuracy. Lay the foundation for driver fatigue detection based on visual.

scholarly journals Introduction and investigation of magnetic location system for steam-assisted gravity drainage dual-horizontal heavy oil wells

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879897 ◽  
Author(s):  
Yong Chen ◽  
Hao Yi ◽  
Chuan He
Keyword(s):  
Measurement Error ◽  
Oil Recovery ◽  
Detection System ◽  
Rapid Development ◽  
Gravity Drainage ◽  
Horizontal Projection ◽  
Recovery Method ◽  
Location System ◽  
Steam Assisted Gravity Drainage ◽  
Magnetic Source

Steam-assisted gravity drainage has been proven to be an effective oil recovery method, and the technology of magnetic location is the key to steam-assisted gravity drainage. In view of the rapid development of this technology in China, a new magnetic location system with intellectual property rights was developed in this article, including mechanical parts and circuit section of detection system. Specific structure, operating principle, and technical parameters of magnetic source generator and detection system were designed and analyzed. The ground test results show that the source generator is powered by an alternating current of 4–7 A, the detection system can probe the magnetic field signal 25 m away from the magnetic source generator, and the measurement error is less than 3% by comparison of measured with actual spacing distance. The steam-assisted gravity drainage dual-horizontal well group in Zhong 37 Well block in Fengcheng Oilfield is chosen for further experiment with the developed magnetic location technology. The results of field experiment show the trajectories of Wells I (injection well) and P (production well) are basically matched in the horizontal projection, and the measurement error is within the allowable range. The magnetic location system developed in this article can meet the operational requirement in steam-assisted gravity drainage dual-horizontal wells.

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