Low-Complexity Pupil Tracking for Sunglasses-Wearing Faces for Glasses-Free 3D HUDs

Kang, Dongwoo; Chang, Hyun Sung

doi:10.3390/app11104366

Cited by 6 publications

(9 citation statements)

References 31 publications

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“…A pose-specific classification system has been also presented in [203] to provide better classification with low computational cost. Kang et al [204] applied real-time pupil localization and tracking of drivers wearing facial accessories including masks. It considers the key requirements of low complexity and algorithm performance by classifying images then assigning the appropriate eye tracker.…”

Section: Algorithm Complexitymentioning

confidence: 99%

Masked Face Recognition Using Deep Learning: A Review

et al. 2021

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A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR.

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Section: Algorithm Complexitymentioning

confidence: 99%

Masked Face Recognition Using Deep Learning: A Review

et al. 2021

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“…E YE tracking, which locates the center of the pupil and estimates gaze direction, is a core technology with diverse applications in various fields. It plays a significant role in attention tracking for market research and advertising [1], enhancing human-robot interaction [2], and enabling advanced features in automotive applications, including driver monitoring systems (DMS) [3] and head-up displays (HUDs) [4], [5]. Additionally, eye tracking finds utility in augmented reality (AR) [6], virtual reality (VR) [7], and three-dimensional (3D) display systems [8], as well as in consumer devices such as mobile smartphones and laptops [8], offering enhanced user interactions and gaming experiences.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding DMS in vehicular applications, eye tracking becomes increasingly important for detecting and monitoring driver status [3]. HUDs enable the generation of natural 3D content aligned with the user's eye position [4], [5]. For AR, VR, and Autostereoscopic 3D display applications, eye tracking is essential in reducing 3D fatigue by ensuring the accurate separation of left and right stereoscopic images, thus providing comfortable viewing experiences [8], [9].…”

Section: Introductionmentioning

confidence: 99%

“…Near-range eye tracking is commonly used in wearable glasses devices, where near-infrared (NIR) LEDs are used to analyze the corneal reflections from the pupils and extract users' gaze information [13], [14]. In contrast, remote eye tracking is mainly required in applications such as AR 3D HUDs [4], [5], autostereoscopic 3D displays [8], [9], and DMS [3]. In these applications, remote eye tracking is impor- tant to rapidly and precisely detect and track the position of the user's eyes at distances of approximately 1 meter, even under varying lighting conditions [8].…”

Section: Introductionmentioning

confidence: 99%

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Event Camera-Based Pupil Localization: Facilitating Training With Event-Style Translation of RGB Faces

Kang,

Kang

2023

IEEE Access

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An innovative approach to pupil tracking using event cameras is presented in this paper. Our method incorporates two primary processes: RGB-to-Event image domain translation and pupil localization utilizing Event Cameras. Initially, we convert traditional RGB images into event-like images with our novel adaptive StyleFlow algorithm. This advanced algorithm enables the generation of images that are remarkably similar to those produced by real event cameras in terms of their distinctive characteristics and visual appeal. Subsequently, we perform the pupil localization process, which involves applying the RetinaFace algorithm. This algorithm is trained using our unique cross-modal learning strategy on a mixed dataset, consisting of both RGB and the newly transformed event-like images. When evaluated using real event camera data, our approach sets a new benchmark in accuracy performance. We achieved a face detection accuracy of 99.4% and a pupil alignment accuracy of 97.2%, exceeding the performance of previous deep learning-based methods that were trained on conventional RGB images. Our results effectively demonstrate the promising potential of event camera-based pupil tracking. Furthermore, our study represents an important advance in the field, offering the possibility of future advancements and potential applications in vehicular systems and augmented reality heads-up displays (AR HUDs).

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“…It was reported to achieve an accuracy of 96.86% (for normalized error less than 0.05) only on the BioID dataset with the maximum processing time per image of 34 𝑚𝑠. In order to meet two critical requirements of low complexity and algorithm performance, Kang and Chang [22] proposed a pupil-tracking approach applicable to both drivers with and without sunglasses. To generate bare faces, a regressionalgorithm-based approach was employed that utilizes scale-invariant feature transforms.…”

Section: -Introductionmentioning

confidence: 99%

A YOLO Detector Providing Fast and Accurate Pupil Center Estimation using Regions Surrounding a Pupil

Kurdthongmee

Suwannarat

et al. 2022

Emerg Sci J

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Eye-tracking technology has many useful applications, including Virtual Reality (VR) devices, Augmented Reality (AR) devices, and assistive technology. The main objective of eye-tracking technology is to detect eye position and track eye movements. It is possible to determine the eye position when the pupil center is detected. In this paper, a deep learning-based approach to the detection of pupil centers from webcam images is presented. As opposed to all previous approaches to object detection based on training the detector with objects to be detected, our object detector was trained with both the region surrounding a pupil and the region between an eye and the region surrounding a pupil. The latter set of regions has been found to increase the overall detection accuracy. A novel post-processing algorithm is also presented to estimate the pupil center from all the detected regions. To achieve real-time performance, we have adopted the tiny architecture of YOLOv3, which has 23 layers and can be executed without requiring a GPU accelerator. To train the detectors, different variations of regions covering a pupil and an eye were used, as well as expanding regions surrounding a pupil and an eye. The PUPPIE dataset was used as the primary input for training the detector. The setting with the best detection accuracy was applied to all publicly available datasets: I2Head, MPIIGaze, and U2Eyes. In terms of accuracy, the results indicate that pupil center estimation is comparable to the state-of-the-art approach. It achieves pupil center estimation errors below the size of a constricted pupil in more than 98.24% of images. Furthermore, the detection time is 2.8 times faster than the state-of-the-art approach. Doi: 10.28991/ESJ-2022-06-05-05 Full Text: PDF

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Low-Complexity Pupil Tracking for Sunglasses-Wearing Faces for Glasses-Free 3D HUDs

Cited by 6 publications

References 31 publications

Masked Face Recognition Using Deep Learning: A Review

Masked Face Recognition Using Deep Learning: A Review

Event Camera-Based Pupil Localization: Facilitating Training With Event-Style Translation of RGB Faces

A YOLO Detector Providing Fast and Accurate Pupil Center Estimation using Regions Surrounding a Pupil

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