Blink Detection Research Articles

Design of Low cost Data Acquisition Circuit with Feature Extraction

Single channel Electrooculogram (EOG) data acquisition system is designed to observe the patterns of signal to confirm the presence of disease or disorder. Tracking the movements of the eyes are done for controlling applications such as wheelchair control, blink detection, word speller, etc for people with complete or partial disability. With the help of signals generated from the eyes, the pattern is studied. This paper lay upon designing of highly precised instrumentation part of single channel EOG with miniaturized circuit and low cost. Different amplifiers are used to remove noise and increase the gain of the signal. Arduino UNO will read the EOG signal as analog input and the signal is recorded using PLX DAQ software which is interfaced with Arduino. The EOG signal is taken for 15 adults and it is tested for the working of the circuit. The signal is further amplified and filtered using MATLAB software and thus the eye movements are visualized. The set of basic mathematical feature are derived for the filtered signal which can be used for future applications for differentiating various eye problems.

Real-time face & eye tracking and blink detection using event cameras

Event cameras contain emerging, neuromorphic vision sensors that capture local-light​ intensity changes at each pixel, generating a stream of asynchronous events. This way of acquiring visual information constitutes a departure from traditional frame-based cameras and offers several significant advantages — low energy consumption, high temporal resolution, high dynamic range and low latency. Driver monitoring systems (DMS) are in-cabin safety systems designed to sense and understand a drivers physical and cognitive state. Event cameras are particularly suited to DMS due to their inherent advantages. This paper proposes a novel method to simultaneously detect and track faces and eyes for driver monitoring. A unique, fully convolutional recurrent neural network architecture is presented. To train this network, a synthetic event-based dataset is simulated with accurate bounding box annotations, called Neuromorphic-HELEN. Additionally, a method to detect and analyse drivers’ eye blinks is proposed, exploiting the high temporal resolution of event cameras. Behaviour of blinking provides greater insights into a driver level of fatigue or drowsiness. We show that blinks have a unique temporal signature that can be better captured by event cameras.

Efficient Haar Wavelet Transform for Detecting Saccades and Blinks in Real-Time EOG Signal

In this study, we present an efficient Haar discrete wavelet transform architecture using Radix- $$2^{r}$$ multiplier and 4:2 compressor. The functionality of this architecture is verified using real-time electrooculogram signal. This architecture is useful for saccade identification and blink detection, as well as for recognizing human eye activity while reading. The standard Haar wavelet structure includes an adder, subtracter, and multiplier. The Haar structure is synthesized the use of the Cadence RTL compiler. The synthesis outcomes display that the proposed structure has much less latency and lower power consumption.

Blink Detection Method for Obstruction Warning Signals

Blink Detection Method for Obstruction Warning Signals

In the blink of an eye: Quantitative blink dynamics predict deceptive personality traits in forensic interviews

It is commonly believed that eye movements and blinks can be indicative of deception, however, there are few laboratory studies of such effects in general and no studies in real-world forensic populations in particular. Here we test the hypothesis that automated eye blink detection and blink frequency dynamics would predict trait levels of deception in a forensic sample. Participants were incarcerated male offenders (n = 125) undergoing life history interviews which were used in part to assess psychopathic personality traits using the Hare Psychopathy Checklist-Revised. Psychopathy is a personality disorder characterized in part by trait dishonesty, manipulation, and pathological lying. We developed a novel automated image analyses approach using hidden Markov machines, where the assumption is that an underlying hidden process influences the observable, measurable process (i.e., deception). Consistent with hypotheses, blink dynamics were predictive of interpersonal psychopathic traits (i.e., pathological lying and conning/manipulative behavior). This study provides the first evidence that blink dynamics from a naturalistic forensic interview, may help understand individual differences in trait deception.

Eyeblink Detection in the Field: A Proof of Concept Study of Two Mobile Optical Eye-Trackers

ABSTRACTIntroductionHigh physical and cognitive strain, high pressure, and sleep deficit are part of daily life for military professionals and civilians working in physiologically demanding environments. As a result, cognitive and physical capacities decline and the risk of illness, injury, or accidents increases. Such unfortunate outcomes could be prevented by tracking real-time physiological information, revealing individuals’ objective fatigue levels. Oculometrics, and especially eyeblinks, have been shown to be promising biomarkers that reflect fatigue development. Head-mounted optical eye-trackers are a common method to monitor these oculometrics. However, studies measuring eyeblink detection in real-life settings have been lacking in the literature. Therefore, this study aims to validate two current mobile optical eye-trackers in an unrestrained military training environment.Materials and MethodThree male participants (age 20.0 ± 1.0) of the Swiss Armed Forces participated in this study by wearing three optical eye-trackers, two VPS16s (Viewpointsystem GmbH, Vienna, Austria) and one Pupil Core (Pupil Labs GmbH, Berlin, Germany), during four military training events: Healthcare education, orienteering, shooting, and military marching. Software outputs were analyzed against a visual inspection (VI) of the video recordings of participants’ eyes via the respective software. Absolute and relative blink numbers were provided. Each blink detected by the software was classified as a “true blink” (TB) when it occurred in the software output and the VI at the same time, as a “false blink” (FB) when it occurred in the software but not in the VI, and as a “missed blink” (MB) when the software failed to detect a blink that occurred in the VI. The FBs were further examined for causes of the incorrect recordings, and they were divided into four categories: “sunlight,” “movements,” “lost pupil,” and “double-counted”. Blink frequency (i.e., blinks per minute) was also analyzed.ResultsOverall, 49.3% and 72.5% of registered eyeblinks were classified as TBs for the VPS16 and Pupil Core, respectively. The VPS16 recorded 50.7% of FBs and accounted for 8.5% of MBs, while the Pupil Core recorded 27.5% of FBs and accounted for 55.5% of MBs. The majority of FBs—45.5% and 73.9% for the VPS16 and Pupil Core, respectively—were erroneously recorded due to participants’ eye movements while looking up, down, or to one side. For blink frequency analysis, systematic biases (±limits of agreement) stood at 23.3 (±43.5) and −4.87 (±14.1) blinks per minute for the VPS16 and Pupil Core, respectively. Significant differences in systematic bias between devices and the respective VIs were found for nearly all activities (P < .05).ConclusionAn objective physiological monitoring of fatigue is necessary for soldiers as well as civil professionals who are exposed to higher risks when their cognitive or physical capacities weaken. However, optical eye-trackers’ accuracy has not been specified under field conditions—especially not in monitoring fatigue. The significant overestimation and underestimation of the VPS16 and Pupil Core, respectively, demonstrate the general difficulty of blink detection in the field.

Unsupervised Eye Blink Artifact Detection From EEG With Gaussian Mixture Model.

Eye blink is one of the most common artifacts in electroencephalogram (EEG) and significantly affects the performance of the EEG related applications, such as epilepsy recognition, spike detection, encephalitis diagnosis, etc. To achieve an accurate and efficient eye blink detection, a novel unsupervised learning algorithm based on a hybrid thresholding followed with a Gaussian mixture model (GMM) is presented in this paper. The EEG signal is priliminarily screened by a cascaded thresholding method built on the distributions of signal amplitude, amplitude displacement, as well as the cross channel correlation. Then, the channel correlation of the two frontal electrodes (FP1, FP2), the fractal dimension, and the mean of amplitude difference between FP1 and FP2, are extracted to characterize the filtered EEGs. The GMM trained on these features is applied for the eye blink detection. The performance of the proposed algorithm is studied on two EEG datasets collected by the Temple University Hospital (TUH) and the Children's Hospital, Zhejiang University School of Medicine (CHZU), where the datasets are recorded from epilepsy and encephalitis patients, and contain a lot of eye blink artifacts. Experimental results show that the proposed algorithm can achieve the highest detection precision and F1 score over the state-of-the-art methods.

Development of Blink Restoration Model for Facial Paralysis Detection

On account of blink feature, patients suffer from facial paralysis that lead to disfiguration and loss of vision. Ultimately, facial pacing aims to improve the the standard of life of individuals suffering from facial unilateral paralysis A device is designed that can analyze electromyography signals from the healthy side of the face eye and trigger the corresponding muscle with electrical stimulation on the paralysed side simultaneously. FES is one of the possible ways for restoration of blink of these patients. The system modelled will achieve the EMG signal extraction and blink detection algorithm for stimulation by real time signals. Finally, the device has been experimented on humans, rabbits, dogs with EMG signal data set. The attenuation of power frequency is about 51dB with 80% accuracy.

Assessment of the Viability of a Biometric Characteristic in the Context of Biometric Authentication on Mobile Devices

The issue of safe utilization of mobile devices is becoming an increasingly important problem, among others due to the widespread use of such devices to access sensitive data (such as electronic documents or banking data). In our work we analyze the use of biometric techniques in order to secure a mobile device, with particular emphasis on the viability of selected biometric characteristics. For this purpose, we investigate the possibility of applying machine learning models to assess the authenticity of a biometric characteristic. Results of our tests have shown that the most effective method of assessing the viability of a biometric characteristic involves blink and smile detection.

Face Anti-Spoofing Method with Blinking Eye and HSV Texture Analysis

A face recognition system should recognize faces and detect spoofing attempts with printed face or digital displays. A straightforward method of spoofing prevention is to analyze life signs such as eye blinking. However, this method is vulnerable when dealing with video-based replay attacks. For this reason, this paper proposes a combined method of blink detection with HSV (Hue, Saturation, Value) texture analysis. The anti-spoofing method is designed with two modules, the blinking eye module that evaluates eye openness points and the HSV texture module, which evaluates the texture of the color space from the input media. The test data is taken from the NUAA Photograph Imposter database. The results show that the designed model successfully recognizes 100% spoof-attack without exception.

Blink detection and magnetic force generation for correction of lagophthalmos, with specific regard to implant compatibility testing

ABSTRACT Purpose The overall goal was to restore a normal and synchronous blink in unilateral lagophthalmos. We describe the biocompatibility profiling of a novel ferromagnetic implant used for electromagnetic eyelid force generation. Methods A non-contact blink detection system and an electromagnetic stimulation system were designed and tested. A modified Lester-Burch speculum equipped with strain gauge technology was used in blinking force measurement. Samarium–cobalt magnets were prototyped and coated with parylene-C. Biocompatibility testing was performed using NIH/3T3 mouse fibroblast cells with MTT colorimetric assay cytotoxic quantification. Outcome Measures Cellular viability and interleukin concentrations. Results Our system was capable of detecting 95.5 ± 3.6% of blinks in various lighting conditions. Using our force measuring device, the difference between non-paralyzed and paralyzed orbicularis oculi (OO) for normal and forceful blinking closure was 40.4 g and 101.9 g, respectively. A 16.6 × 5.0 × 1.5 mm curved shaped samarium cobalt eyelid implant was successfully developed and showed a reproducible blink at 100 ms with full corneal coverage with external eyelid taping. Compared to gold weights, parylene-C coated samarium cobalt implants showed not only excellent cell viability (82.0 ± 4.9% vs. 88.4 ± 0.9%, respectively, p > .05), but also below detection threshold for pro-inflammatory marker concentrations (interleukin-6 < 2 pg/mL and interleukin-10 < 3 pg/mL). Conclusions We demonstrated excellent in-vitro biocompatibility of our parylene-C coated samarium cobalt implants. We believe that our novel approach can improve the quality-of-life of affected individuals and provides new understanding of blinking biomechanics.

A Smart Drowsiness Detection System for Accident Prevention

Driver’s drowsiness is one of the main reasons for road accidents which lead to several fatalities every year. Statistics highlights the necessity of a drowsiness detection system that could possibly alert the co-passengers and driver before an accident would occur. Some automobile manufacturers have implemented drowsiness detection systems in their cars that work based on the vehicle movement, angle of steering wheel and other factors, but these parameters may not be reliable while detecting drowsiness of a driver. Few research papers have published on determining the drowsiness of a driver using electroencephalogram (EEG) sensor technology that detects the brain waves and determines if the person drowsy based on the obtained data. EEG is an optimum way of measuring the active state of a person in real time. In this work, we are adopting this technology along with an eye blink detection to detect the drowsiness and alert the driver.

Multimodal human eye blink recognition method using feature level fusion for exigency detection

In this paper, a precise multimodal eye blink recognition method using feature level fusion (MmERMFLF) is proposed. A new feature: eye-eyebrow facet ratio (EEBFR) (formed by fusing eye facet ratio (EFR: ratio of diagonal length and width of eye) and eyebrow to nose facet ratio (EBNFR: distance between eyebrow landmarks and nose landmark)) for approximating the eye state is computed. Initially, an improved intellectual framework (Sagacious Information Recuperation Technique) that senses the emergency state using information retrieved from eye blinks, pulse rate as well as behavioral patterns(emotions) exhibited by an individual is presented. Further a novel multimodal method (MmERMFLF) for detection and counting of eye blinks is implemented. For training, one state-of-the-art database—ZJU is used. To additionally improve the performance, feature-level fusion schemes [simple concatenate and fusion codes (gaborization)] are enforced and equated. Receiver operating characteristics, error rate, sensitivity, specificity, and precision are used to demonstrate the performance of the proposed method qualitatively and quantitatively. Accuracy with proposed MmERMFLF is increased to 99.02% (using EEBGFR method with bagged ensemble classifier) in comparison to unimodal eye blink recognition system (97.60%). 99.80% genuine blinks are classified by MmERMFLF (when gaborization fusion is used) using simple tree classifier.

Eye Detection System Based on Image Processing for Vehicle Safety

The more advanced the technology and the greater the community's need to carry out activities every day, the number of vehicles on the highway is getting crowded. From year to year, the greater the level of traffic accidents caused by many factors, among the usual reasons is the loss of awareness of the driver when driving a vehicle especially drowsiness. One of the drowsiness parameters is the frequency eye blinks. Therefore, to get the drowsiness symptoms, the purpose of this research is to detect the eye blinks, which in turn reduce the level of accidents by detecting sleepy eyes based on digital image processing. The method used to detect both eyes is the Viola-Jones method. The detection of both eyes can also acquire the duration of closed eyes and the number of eye blinks. A person can be said to be sleepy by means of sleepiness parameters determined by a study. The research shows that detection of eye blinks using the Viola-Jones method has a fairly high accuracy of up to 84.72% if the face condition is upright and tilted no more than 45 degrees. Another conclusion is that eye detection and driver detection are more effective at certain light intensity values which are around 2-33 lux.

Eye Blink Detection System for Paralyzed Patients

Paralysis of an human being is caused due to the degeneration of motor neurons which weakens the muscle, so that it does not allow patient to move, speak, breathe , and loss in the voluntary actions. It is an incurable disease. To understand the feelings of a paralyzed patients Brain wave technique and Electro-oculography techniques were used. These techniques are afflictive, discomfortable and leads to unconsciousness of the paralyzed patient. The real time video oculography system fills the communication gap between the patient and the world. Video Oculography (VOG) is video-based method of measuring the vertical, torsional and horizontal position components of both the eye blinks with the help of small cameras placed in the head-mounted mask .This paper presents different visual technologies, such as eye blink detection, eye center localization and conversion of the eye blink to speech. The video oculography could achieve accuracy of 0.968.

A Hybrid Asynchronous Brain-Computer Interface Combining SSVEP and EOG Signals.

A challenging task for an electroencephalography (EEG)-based asynchronous brain-computer interface (BCI) is to effectively distinguish between the idle state and the control state while maintaining a short response time and a high accuracy when commands are issued in the control state. This study proposes a novel hybrid asynchronous BCI system based on a combination of steady-state visual evoked potentials (SSVEPs) in the EEG signal and blink-related electrooculography (EOG) signals. Twelve buttons corresponding to 12 characters are included in the graphical user interface (GUI). These buttons flicker at different fixed frequencies and phases to evoke SSVEPs and are simultaneously highlighted by changing their sizes. The user can select a character by focusing on its frequency-phase stimulus and simultaneously blinking his/her eyes in accordance with its highlighting as his/her EEG and EOG signals are recorded. A multifrequency band-based canonical correlation analysis (CCA) method is applied to the EEG data to detect the evoked SSVEPs, whereas the EOG data are analyzed to identify the user's blinks. Finally, the target character is identified based on the SSVEP and blink detection results. Ten healthy subjects participated in our experiments and achieved an average information transfer rate (ITR) of 105.52 bits/min, an average accuracy of 95.42%, an average response time of 1.34s and an average false-positive rate (FPR) of 0.8%. The proposed BCI generates multiple commands with a high ITR and low FPR. The hybrid asynchronous BCI has great potential for practical applications in communication and control.

Design and Implementation of Artificial Human Hand Model Based on Eye Blink Detection

This paper proposes two paradigms for controlling a robotic arm by integrating eye blink and IOT based techniques. The purpose of our study is to develop a feasible paradigm for helping disabled persons with their every-day needs.“Artificial Human Hand Model (AHHM)” which can precisely follow the real movements of human hand by means of real human eye blinks and IOT based methods. Simple algorithms were implemented for detecting electrophysiological signals like eye saccades, blinking and eye closure events. Interactive resources to integrate myRIO into our project increases efficiency of this concept.

Blink Detection Based on Pixel Fluctuation Ratio of Eye Image

In this paper, an algorithm of real-time eye blink monitoring using ordinary camera is proposed. This algorithm is based on pixel fluctuation ratio (FRP) of eye region image to detect eye blink. Position the eye area according to the facial landmark algorithm and then convert the image to a grayscale image. According to the point detected by the eye region in the grayscale image, the eye region is divided into three segments from the corner of the eye to the end of the eye. Calculate the value of the pixels in each segment, and find the partial image pixel value fluctuation ratio (FRP), and train the selected training samples with the support vector machine (SVM). We tested it on two data sets, and the blink detection algorithm achieved good results on both data sets.

A Brain Computer Interface based Communication System using SSVEP and EOG

This study aims to design a high performance communication system using steady state visual evoked potential (SSVEP) and electro-oculogram (EOG) signal. The proposed keyboard system consists of twenty three randomly chosen characters and they were indexed into different flickering visual stimuli designed using three SSVEP stimulus frequencies. The same frequency valued visual stimuli were used many times (more than once) in the keyboard layout. In this approach an oddball paradigm is introduced and it randomly highlights three/two unique frequency valued visual stimuli at a time. The system identifies a desired target by user eye blink in accordance to the oddball paradigm and recognized SSVEP frequency value. The signal to noise ratio (SNR) of the real time SSVEP electroencephalogram signal gets increased by the introduction of oddball paradigm in the SSVEP based keyboard system. The paradigm will increase the subject’s attention and concentration on the flickering target stimulus. The real time SSVEP data were classified using extended multivariate synchronization index (EMSI) method. The thresholding method is used for single blink detection. The offline and online experiments were performed by all the subjects for evaluating the proposed communication system. The average classification accuracy and information transfer rate of the system are 96.73 % and 76.02 bits/min respectively.

Detection of eye closing/opening from EOG and its application in robotic arm control

Detection of eye closing/opening from alpha-blocking in the EEG of occipital region has been used to build human-machine interfaces. This paper presents an alternative method for detection of eye closing/opening from EOG signals in an online setting. The accuracies for correct detection of eye closing and opening operations with the proposed techniques were found to be 95.6% and 91.9% respectively for 8 healthy subjects. These techniques were then combined with the detection of eye blinks, the accuracy of which turned out to be 96.9%. This was then used to build an interface for robotic arm control for a pick and place task. The same task was also carried out using a haptic device as a master. The speed and accuracy for these two methods were then compared to assess quantitatively the ease of using this interface. It appears that the proposed interface will be very useful for persons with neurodegenerative disorders who can perform eye closing/opening and eye blinks.