Deep Neural Networks for Low-Cost Eye Tracking

Rakhmatulin, Ildar; Duchowski, Andrew T.

doi:10.1016/j.procs.2020.09.041

Cited by 31 publications

(9 citation statements)

References 17 publications

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“…We have recently shown that an adaptation of current deep-learning methodologies for eye tracking 48 , 49 , together with a dual-camera system, can determine gaze to discriminate specific facial areas and quantify movement synchronization 42 . The camera system has been developed with neurodevelopmental disorders in mind; it is minimal, does not include any wearables, such as eye-tracking glasses, and is located on a table between two individuals without covering the face of the person in front of them, see Fig.…”

Section: Methodsmentioning

confidence: 99%

Influence of autistic traits and communication role on eye contact behavior during face-to-face interaction

Thorsson,

Galazka,

Åsberg Johnels

et al. 2024

Sci Rep

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Eye contact is a central component in face-to-face interactions. It is important in structuring communicative exchanges and offers critical insights into others' interests and intentions. To better understand eye contact in face-to-face interactions, we applied a novel, non-intrusive deep-learning-based dual-camera system and investigated associations between eye contact and autistic traits as well as self-reported eye contact discomfort during a referential communication task, where participants and the experimenter had to guess, in turn, a word known by the other individual. Corroborating previous research, we found that participants’ eye gaze and mutual eye contact were inversely related to autistic traits. In addition, our findings revealed different behaviors depending on the role in the dyad: listening and guessing were associated with increased eye contact compared with describing words. In the listening and guessing condition, only a subgroup who reported eye contact discomfort had a lower amount of eye gaze and eye contact. When describing words, higher autistic traits were associated with reduced eye gaze and eye contact. Our data indicate that eye contact is inversely associated with autistic traits when describing words, and that eye gaze is modulated by the communicative role in a conversation.

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Section: Methodsmentioning

confidence: 99%

Influence of autistic traits and communication role on eye contact behavior during face-to-face interaction

Thorsson,

Galazka,

Åsberg Johnels

et al. 2024

Sci Rep

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show abstract

“…In 2020, I. Rakhmatulin and A. T. Duchowski, presented an analysis with details about modern techniques that used a webcam for gaze-tracking, details about the practical implementation of the populist methods for gaze-tracking, and an introduction of a new eye-tracking approach that significantly increased the effectiveness of using a deep learning method [13].…”

Section: Related Workmentioning

confidence: 99%

Improvement of Eye Tracking Based on Deep Learning Model for General Purpose Applications

Almindelawy

Ali

2022

NJES

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The interest in the Eye-tracking technology field dramatically grew up in the last two decades for different purposes and applications like keeping the focus of where the person is looking, how his pupils and irises are reacting for a variety of actions, etc. The resulted data can deliver an extraordinary amount of information about the user when it's interlocked through advanced data analysis systems, it may show information concerned with the user’s age, gender, biometric identity, interests, etc. This paper is concerned about eye motion tracking as an unadulterated tool for different applications in any field required. The improvements in this area of artificial intelligence (AI), machine learning (ML), and deep learning (DL) with eye-tracking techniques allow large opportunities to develop algorithms and applications. In this paper number of models were proposed based on Convolutional neural network (CNN) have been designed, and then the most powerful and accurate model was chosen. The dataset used for the training process (for 16 screen points) consists of 2800 training images and 800 test images (with an average of 175 training images and 50 test images for each spot on the screen of the 16 spots), and it can be collected by the user of any application based on this model. The highest accuracy achieved by the best model was (91.25%) and the minimum loss was (0.23%). The best model consists of (11) layers (4 convolutions, 4 Max pooling, and 3 Dense). Python 3.7 was used to implement the algorithms, KERAS framework for the deep learning algorithms, Visual studio code as an Integrated Development Environment (IDE), and Anaconda navigator for downloading the different libraries. The model was trained with data that can be gathered using cameras of laptops or PCs and without the necessity of special and expensive equipment, also It can be trained for any single eye, depending on application requirements.

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“…The authors achieved a correlation between the events before and after blinking [25]. A similar approach was executed by Ildar et al, where neural boundaries captured the relative face and pupil positions while the subjects were asked to follow the mouse pointer [26]. This way, voluntary blinking motions were captured.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Eye Tracking Using Heat Maps

Krishnan

Jeyakumar²,

Raj³

2022

MJCS

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Communication in modern days has developed a lot, including wireless networks, Artificial Intelligence (AI) interaction, and human-computer interfaces. People with paralysis and immobile disorders face daily difficulties communicating with others and gadgets. Eye tracking has proven to promote accessible and accurate interaction compared to other complex automatic interactions. The project aims to develop an electronic eye blinker that integrates with the experimental setup to determine clinical pupil redundancy. The proposed solution comes up with an eye-tracking tool within an inbuilt laptop webcam that tracks the eye’s pupil in the given screen dimensions and generates heat maps on the tracked locations. These heat maps can denote a letter (in case of eye writing), an indication to click on that location (in case of gadget communication), or for blinking analysis. The proposed method achieves a perfect F-measure score of 0.998 to 1.000, which is comparatively more accurate and efficient than the existing technologies. The solution also provides an effective method to determine the eye's refractive error, which can replace the complex refractometers. Further, the spatially tracked coordinates obtained during the experiment can be used to analyze the patient’s blinking pattern, which, in turn, can detect retinal disorders and their progress during medication. One of the applications of the project is to integrate the derived model with a Brain-computer interface system to allow fast communications for the disabled.

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Deep Neural Networks for Low-Cost Eye Tracking

Cited by 31 publications

References 17 publications

Influence of autistic traits and communication role on eye contact behavior during face-to-face interaction

Influence of autistic traits and communication role on eye contact behavior during face-to-face interaction

Improvement of Eye Tracking Based on Deep Learning Model for General Purpose Applications

Real-Time Eye Tracking Using Heat Maps

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