Real-time Eye Detection Research Articles

A novel real-time eye detection method using edge detection and Euclidean distance

A novel real-time eye detection method using edge detection and Euclidean distance

Authenticity detection of eye images using deep learning technology in online exams

The use of online education technologies is one of the most effective ways to achieve the universal goal of quality education throughout life for the sustainable development of UNESCO. During the writing of the article, the features that have appread in the field of application of online exam technologies will be described. The existing forms, methods and tools for conducting the exam in an online format, their potential and risks are analyzed. Attention is drawn to the fact that the methods and tools used for conducting online control have disadvantages, such as significant costs in organizing proctoring and difficulties in assessing one of the student’s key competencies, namely, readiness to use systematized theoretical and practical knowledge to formulate and solve research problems. This research introduces an innovative proctoring system that harnesses deep learning techniques, specifically the Yolo algorithm. The primary objective of this system is to accurately detect the eyes of the exam-taker by utilizing a standard web camera as the sole input source. The obtained results from the deep learning model are subsequently subjected to processing through the Cascaded Haar Classifier to yield a definitive outcome. The proposed approach demonstrates considerable proficiency in discriminating between authentic exam-takers and static images, achieving a notable level of accuracy. Leveraging the capabilities of deep learning in conjunction with the cascaded classifier enables real-time eye detection and localization within images or video frames, thereby establishing the proctoring system as a reliable and effective solution for ensuring academic integrity during online examinations.

Intelligent Vehicle Black Box System Using IoT

Humans are currently involved in numerous transportation-related accidents on the roads. Numerous accidents and fatalities caused by human error are the result of the rising number of vehicles on Indian roads and a lack of enforcement of traffic laws. They also suffer loss of life and valuable property as a result of those accidents. As a result, accidents involving driver inattention to traffic laws and driver fatigue follow. The field of computer vision is actively researching real-time eye detection and tracking. Face alignment can benefit from eye localization and tracking. The duration of eye closure and percentage of eye closure (PEC) can be used to measure driver fatigue. It is based on a hardware system that uses a camera to capture images of the driver in real time and software to monitor the driver's eye in order to prevent accidents. In the event of an accident, GSM and GPS are utilised to track the location of the automobile, and the local hospital and police are alerted. Thanks to IoT technology, this position may always be found in the cloud platform service. The 24/7 Governance is notified to call for emergency assistance by using the push and panic button.

Correction: A colorimetric probe for the real-time naked eye detection of cyanide and hydroxide ions in tap water: experimental and theoretical studies.

Correction for 'A colorimetric probe for the real-time naked eye detection of cyanide and hydroxide ions in tap water: experimental and theoretical studies' by Veikko Uahengo et al., Analyst, 2019, 144, 6422-6431.

RETRACTED ARTICLE: Real-time eye detection in multimedia environment

RETRACTED ARTICLE: Real-time eye detection in multimedia environment

Eye detection using discriminatory Haar features and a new efficient SVM

This paper presents an accurate and efficient eye detection method using the discriminatory Haar features (DHFs) and a new efficient support vector machine (eSVM). The DHFs are extracted by applying a discriminating feature extraction (DFE) method to the 2D Haar wavelet transform. The DFE method is capable of extracting multiple discriminatory features for two-class problems based on two novel measure vectors and a new criterion in the whitened principal component analysis (PCA) space. The eSVM significantly improves the computational efficiency upon the conventional SVM for eye detection without sacrificing the generalization performance. Experiments on the Face Recognition Grand Challenge (FRGC) database and the BioID face database show that (i) the DHFs exhibit promising classification capability for eye detection problem; (ii) the eSVM runs much faster than the conventional SVM; and (iii) the proposed eye detection method achieves near real-time eye detection speed and better eye detection performance than some state-of-the-art eye detection methods.

An FPGA-based Parallel Hardware Architecture for Real-time Eye Detection

Eye detection is widely used in applications, such as face recognition, driver behavior analysis, and human-computer interaction. However, it is difficult to achieve real-time performance with software-based eye detection in an embedded environment. In this paper, we propose a parallel hardware architecture for real-time eye detection. We use the AdaBoost algorithm with modified census transform(MCT) to detect eyes on a face image. We parallelize part of the algorithm to speed up processing. Several downscaled pyramid images of the eye candidate region are generated in parallel using the input face image. We can detect the left and the right eye simultaneously using these downscaled images. The sequential data processing bottleneck caused by repetitive operation is removed by employing a pipelined parallel architecture. The proposed architecture is designed using Verilog HDL and implemented on a Virtex-5 FPGA for prototyping and evaluation. The proposed system can detect eyes within 0.15 ㎳ in a VGA image.

Real-Time Eye Detection and Tracking under Various Light Conditions

The CODATA Data Science Journal is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, the humanities and the arts. The scope of the journal includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for open data.All data is in scope, whether born digital or converted from other sources.

An embedded system for an eye-detection sensor

Real-time eye detection is important for many HCI applications, including eye-gaze tracking, autostereoscopic displays, video conferencing, face detection, and recognition. Current commercial and r...

Robust real-time eye detection and tracking under variable lighting conditions and various face orientations

Most eye trackers based on active IR illumination require distinctive bright pupil effect to work well. However, due to a variety of factors such as eye closure, eye occlusion, and external illumination interference, pupils are not bright enough for these methods to work well. This tends to significantly limit their scope of application. In this paper, we present an integrated eye tracker to overcome these limitations. By combining the latest technologies in appearance-based object recognition and tracking with active IR illumination, our eye tracker can robustly track eyes under variable and realistic lighting conditions and under various face orientations. In addition, our integrated eye tracker is able to handle occlusion, glasses, and to simultaneously track multiple people with different distances and poses to the camera. Results from an extensive experiment shows a significant improvement of our technique over existing eye tracking techniques.

An embedded system for an eye-detection sensor

Real-time eye detection is important for many HCI applications, including eye-gaze tracking, autostereoscopic displays, video conferencing, face detection, and recognition. Current commercial and research systems use software implementation and require a dedicated computer for the image-processing task—a large, expensive, and complicated-to-use solution. In order to make eye-gaze tracking ubiquitous, the system complexity, size, and price must be substantially reduced. This paper presents a hardware-based embedded system for eye detection, implemented using simple logic gates, with no CPU and no addressable frame buffers. The image-processing algorithm was redesigned to enable highly parallel, single-pass image-processing implementation. A prototype system uses a CMOS digital imaging sensor and an FPGA for the image processing. It processes 640 × 480 progressive scan frames at a 60 fps rate, and outputs a compact list of sub-pixel accurate ( x, y) eyes coordinates via USB communication. Experimentation with detection of human eyes and synthetic targets are reported. This new logic design, operating at the sensor’s pixel clock, is suitable for single-chip eye detection and eye-gaze tracking sensors, thus making an important step towards mass production, low cost systems.