RT-BENE: A Dataset and Baselines for Real-Time Blink Estimation in Natural Environments

Cortacero, Kevin; Fischer, Tobias; Demiris, Yiannis

doi:10.1109/iccvw.2019.00147

Cited by 35 publications

(23 citation statements)

References 48 publications

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“…Other datasets, instead, include face and eyes images collected in natural environments [ 29 ] or with commercially available devices, such as smartphones [ 30 ], thus to solve most common eye gaze estimation during everyday tasks, such as driving [ 31 ], reading [ 32 ], or browsing the internet [ 33 ]. Other studies include images for the detection of natural blinks [ 34 , 35 ], some of them with the aim of improving gaze estimation [ 36 ].…”

Section: Related Workmentioning

confidence: 99%

Vision-Based Eye Image Classification for Ophthalmic Measurement Systems

Gibertoni

Borghi

Rovati

2022

Sensors

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The accuracy and the overall performances of ophthalmic instrumentation, where specific analysis of eye images is involved, can be negatively influenced by invalid or incorrect frames acquired during everyday measurements of unaware or non-collaborative human patients and non-technical operators. Therefore, in this paper, we investigate and compare the adoption of several vision-based classification algorithms belonging to different fields, i.e., Machine Learning, Deep Learning, and Expert Systems, in order to improve the performance of an ophthalmic instrument designed for the Pupillary Light Reflex measurement. To test the implemented solutions, we collected and publicly released PopEYE as one of the first datasets consisting of 15 k eye images belonging to 22 different subjects acquired through the aforementioned specialized ophthalmic device. Finally, we discuss the experimental results in terms of classification accuracy of the eye status, as well as computational load analysis, since the proposed solution is designed to be implemented in embedded boards, which have limited hardware resources in computational power and memory size.

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Section: Related Workmentioning

confidence: 99%

Vision-Based Eye Image Classification for Ophthalmic Measurement Systems

Gibertoni

Borghi

Rovati

2022

Sensors

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“…In [38], an algorithm was developed for finding the eye region landmarks and computing the EAR, which was then used with a support vector machine to determine whether the eye was open or closed. In addition, a study by [41] was carried out to detect whether the eye was open or closed in each frame by using a pre-trained convolutional neural network to label the eye region on a few hundred annotated images.…”

Section: Eye Blink Detection Techniquesmentioning

confidence: 99%

Eye Blink Rate Based Detection of Cognitive Impairment Using In-the-wild Data

Alzahrani

Mirheidari

Blackburn

et al. 2021

2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII)

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Investigating automatic methods for the early detection of dementia and related conditions that cause cognitive impairment is an area of growing interest. Video processing could play a role by providing a non-invasive and low-cost alternative to current expensive assessments. For this to be successful it is crucial that approaches are robust to in-the-wild challenges. In this paper, visual cues, related to the eye blink rate (EBR), are investigated to quantify the early phase of neurodegenerative disorder (ND) and mild cognitive impairment (MCI) as well as functional memory disorder (FMD; problems with memory not related to neurodegenerative disorder). This paper aims to improve the detection of ND and MCI by investigating a novel approach to calculating the EBR that is more robust to in-thewild challenges. An in-house dataset with 18 participants is used. The EBR is calculated from eye landmarks extracted using two libraries (Dlib and Openface). To mitigate issues observed in the noisy, in-the-wild recordings, a multiple threshold approach for EBR detection is proposed. It involves generating multiple thresholds for identifying a blink, where a threshold is used to determine whether an eye is open or closed. Several supervised machine learning approaches are used for automatic classification. The results show that accuracy measures of 89% and 78% are achieved using Dlib and OpenFace data, respectively, when distinguishing between three conditions with ND, MCI and FMD.

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“…Then, there are traditional ML-based methods using HOG [12] feature, motion [13] feature, etc. With the rise of deep learning, researchers have explored several CNN-based approaches recently, such as [13][14][15]. We also use a CNN-based architecture for this problem.…”

Section: Related Workmentioning

confidence: 99%

Eye-focused Detection of Bell’s Palsy in Videos

Ansari

Jerripothula

Nagpal³

et al. 2021

Proceedings of the Canadian Conference on Artificial Intelligence

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In this paper, we present how Bell's Palsy, a neurological disorder, can be detected just from a subject's eyes in a video. We notice that Bell's Palsy patients often struggle to blink their eyes on the affected side. As a result, we can observe a clear contrast between the blinking patterns of the two eyes. Although previous works did utilize images/videos to detect this disorder, none have explicitly focused on the eyes. Most of them require the entire face. One obvious advantage of having an eye-focused detection system is that subjects' anonymity is not at risk. Also, our AI decisions are based on simple blinking patterns, making them explainable and straightforward. Specifically, we develop a novel feature called blink similarity, which measures the similarity between the two blinking patterns. Our extensive experiments demonstrate that the proposed feature is quite robust, for it helps in Bell's Palsy detection even with very few labels. Our proposed eye-focused detection system is not only cheaper but also more convenient than several existing methods.

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RT-BENE: A Dataset and Baselines for Real-Time Blink Estimation in Natural Environments

Cited by 35 publications

References 48 publications

Vision-Based Eye Image Classification for Ophthalmic Measurement Systems

Vision-Based Eye Image Classification for Ophthalmic Measurement Systems

Eye Blink Rate Based Detection of Cognitive Impairment Using In-the-wild Data

Eye-focused Detection of Bell’s Palsy in Videos

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