mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation

Daza, Roberto; Morales, Aythami; Fiérrez, Julián; Tolosana, Rubén

doi:10.1145/3395035.3425257

Cited by 36 publications

(35 citation statements)

References 27 publications

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“…First of all, we evaluate the performance of the proposed eye blink detector. The evaluation is performed with two databases: mEBAL (Daza et al 2020) and HUST-LEBW benchmark (Hu et al 2019). The mEBAL database is used to train our blink detector and considers a controlled environment, while the HUST-LEBW dataset is obtained in an unconstrained environment.…”

Section: Eye Blink Detection Resultsmentioning

confidence: 99%

“…In this work the mEBAL database 1 is employed (Daza et al 2020). This database was acquired with an experimental elearning platform for remote education assessment called edBB (Hernandez-Ortega et al 2020a).…”

Section: Database For Attention Level Estimation: Mebalmentioning

confidence: 99%

“…One of the most important aspects is the absence of a direct contact between teachers and students. As a result, recent elearning platforms (Hernandez-Ortega et al 2020a) allow to incorporate novel technologies to estimate different factors such as the attention level (Daza et al 2020), the heart rate (Hernandez-Ortega et al 2020b), the emotional state (Shen, Wang, and Shen 2009), and the gaze and head pose (Asteriadis et al 2009).…”

Section: Introductionmentioning

confidence: 99%

“…However, the potential use of automatic eye blink detectors to infer the attention level of users has not been evaluated in realistic scenarios. The existing studies are limited to initial qualitative analyses that suggest the correlation between signals (Daza et al 2020). Furthermore, eye blink detection can be used for fraud/cheating/lies detection as the eye blink rate decreases when cognitive demand increases (Mann, Vrij, and Bull 2002).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

ALEBk: Feasibility Study of Attention Level Estimation via Blink Detection applied to e-Learning

Daza¹,

DeAlcala²,

Morales³

et al. 2021

Preprint

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This work presents a feasibility study of remote attention level estimation based on eye blink frequency. We first propose an eye blink detection system based on Convolutional Neural Networks (CNNs), very competitive with respect to related works. Using this detector, we experimentally evaluate the relationship between the eye blink rate and the attention level of students captured during online sessions. The experimental framework is carried out using a public multimodal database for eye blink detection and attention level estimation called mEBAL, which comprises data from 38 students and multiples acquisition sensors, in particular, i) an electroencephalogram (EEG) band which provides the time signals coming from the student's cognitive information, and ii) RGB and NIR cameras to capture the students face gestures. The results achieved suggest an inverse correlation between the eye blink frequency and the attention level. This relation is used in our proposed method called ALEBk for estimating the attention level as the inverse of the eye blink frequency. Our results open a new research line to introduce this technology for attention level estimation on future e-learning platforms, among other applications of this kind of behavioral biometrics based on face analysis.

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Section: Eye Blink Detection Resultsmentioning

confidence: 99%

Section: Database For Attention Level Estimation: Mebalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ALEBk: Feasibility Study of Attention Level Estimation via Blink Detection applied to e-Learning

Daza¹,

DeAlcala²,

Morales³

et al. 2021

Preprint

Self Cite

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show abstract

“…The authors use the stackbased autoencoders (SAEs) deep learning (DL) model by applying a multimodal fusion approach. In reference [27], the authors develop a multimodal features-based dataset based on the identification of eye blinks and the measurement of attention levels known as mEBAL. The authors deploy two cameras near-infrared (NIR) and RGB to capture vision features and EEG sensors.…”

Section: Literature Reviewmentioning

confidence: 99%

Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

Riquelme¹,

Olivares²,

Mu駉z³

et al. 2022

Computers, Materials &Amp; Continua

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Traffic accidents are caused by driver fatigue or distraction in many cases. To prevent accidents, several low-cost hypovigilance (hypo-V) systems were developed in the past based on a multimodal-hybrid (physiological and behavioral) feature set. Similarly in this paper, real-time driver inattention and fatigue (Hypo-Driver) detection system is proposed through multi-view cameras and biosignal sensors to extract hybrid features. The considered features are derived from non-intrusive sensors that are related to the changes in driving behavior and visual facial expressions. To get enhanced visual facial features in uncontrolled environment, three cameras are deployed on multiview points (0 • , 45 • , and 90 • ) of the drivers. To develop a Hypo-Driver system, the physiological signals (electroencephalography (EEG), electrocardiography (ECG), electro-myography (sEMG), and electrooculography (EOG)) and behavioral information (PERCLOS70-80-90%, mouth aspect ratio (MAR), eye aspect ratio (EAR), blinking frequency (BF), head-titled ratio (HT-R)) are collected and pre-processed, then followed by feature selection and fusion techniques. The driver behaviors are classified into five stages such as normal, fatigue, visual inattention, cognitive inattention, and drowsy. This improved hypo-Driver system utilized trained behavioral features by a convolutional neural network (CNNs), recurrent neural network and long short-term memory (RNN-LSTM) model is used to extract physiological features. After fusion of these features, the Hypo-Driver system is classified hypo-V into five stages based on trained layers and dropout-layer in the deep-residual neural network (DRNN) model. To test the performance of a hypo-Driver system, data from 20 drivers are acquired. The results of Hypo-Driver compared to state-of-theart methods are presented. Compared to the state-of-the-art Hypo-V system, on average, the Hypo-Driver system achieved a detection accuracy (AC) of 96.5%. The obtained results indicate that the Hypo-Driver system based on multimodal and multiview features outperforms other state-of-the-art driver Hypo-V systems by handling many anomalies.

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Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances

Hernandez-Ortega

Fiérrez

Morales

et al. 2023

Handbook of Biometric Anti-Spoofing

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mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation

Cited by 36 publications

References 27 publications

ALEBk: Feasibility Study of Attention Level Estimation via Blink Detection applied to e-Learning

ALEBk: Feasibility Study of Attention Level Estimation via Blink Detection applied to e-Learning

Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances

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