Webcam-based Pupil Diameter Prediction Benefits from Upscaling

• URL: http://arxiv.org/abs/2408.10397v1
• Date: Mon, 19 Aug 2024 20:28:39 GMT
• Title: Webcam-based Pupil Diameter Prediction Benefits from Upscaling
• Authors: Vijul Shah, Brian B. Moser, Ko Watanabe, Andreas Dengel,
• Abstract summary: This study evaluates the impact of various upscaling methods, ranging from bicubic to advanced super-resolution, on pupil diameter predictions. Our findings suggest that pupil diameter prediction models trained on upscaled datasets are highly sensitive to the selected upscaling method and scale.
• Score: 6.298516754485939
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Capturing pupil diameter is essential for assessing psychological and physiological states such as stress levels and cognitive load. However, the low resolution of images in eye datasets often hampers precise measurement. This study evaluates the impact of various upscaling methods, ranging from bicubic interpolation to advanced super-resolution, on pupil diameter predictions. We compare several pre-trained methods, including CodeFormer, GFPGAN, Real-ESRGAN, HAT, and SRResNet. Our findings suggest that pupil diameter prediction models trained on upscaled datasets are highly sensitive to the selected upscaling method and scale. Our results demonstrate that upscaling methods consistently enhance the accuracy of pupil diameter prediction models, highlighting the importance of upscaling in pupilometry. Overall, our work provides valuable insights for selecting upscaling techniques, paving the way for more accurate assessments in psychological and physiological research.

Related papers

• Towards Better Cephalometric Landmark Detection with Diffusion Data Generation [43.381003080365254]
  We develop an innovative data generation method capable of producing diverse cephalometric X-ray images along with corresponding annotations without human intervention.<n>By leveraging detailed prompts, our method improves the generation process to control different styles and attributes.<n>Compared to methods without using the generated data, our approach improves the Success Detection Rate (SDR) by 6.5%, attaining a notable 82.2%.
  arXiv  Detail & Related papers  (2025-05-09T13:50:27Z)
• Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories [4.605794646684244]
  We introduce a novel personalized deep kernel regression framework for forecasting brain biomarkers. Our approach integrates two key components: a population model that captures brain trajectories from a large cohort, and a subject-specific model that captures individual trajectories.
  arXiv  Detail & Related papers  (2025-04-10T19:13:44Z)
• Data Augmentation via Latent Diffusion for Saliency Prediction [67.88936624546076]
  Saliency prediction models are constrained by the limited diversity and quantity of labeled data. We propose a novel data augmentation method for deep saliency prediction that edits natural images while preserving the complexity and variability of real-world scenes.
  arXiv  Detail & Related papers  (2024-09-11T14:36:24Z)
• Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery [46.27328641616778]
  Domain Adaptation (DA) can be used to enhance imagery prediction in fMRI scans. We develop several models to improve imagery prediction, comparing different DA methods. Our DA-enhanced searchlight predicts imagery contents in a highly distributed set of brain regions, including the visual cortex and the frontoparietal cortex.
  arXiv  Detail & Related papers  (2024-08-02T10:25:19Z)
• EyeDentify: A Dataset for Pupil Diameter Estimation based on Webcam Images [6.298516754485939]
  EyeDentify is a dataset specifically designed for pupil diameter estimation based on webcam images. Deep learning models that can estimate pupil diameters using standard webcam data are scarce. EyeDentify enables the development and refinement of models designed specifically for less-equipped environments.
  arXiv  Detail & Related papers  (2024-07-15T19:39:28Z)
• DeepTechnome: Mitigating Unknown Bias in Deep Learning Based Assessment of CT Images [44.62475518267084]
  We debias deep learning models during training against unknown bias. We use control regions as surrogates that carry information regarding the bias. Applying the proposed method to learn from data exhibiting a strong bias, it near-perfectly recovers the classification performance observed when training with corresponding unbiased data.
  arXiv  Detail & Related papers  (2022-05-26T12:18:48Z)
• Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications [8.69535649683089]
  We show how contrastive self-supervised learning can reduce the annotation effort within digital pathology. Our results pave the way for realizing the full potential of self-supervised learning for histopathology applications.
  arXiv  Detail & Related papers  (2021-12-10T16:08:57Z)
• Pathological Analysis of Blood Cells Using Deep Learning Techniques [0.0]
  A neural based network has been proposed for classification of blood cells images into various categories. The performance of proposed model is better than existing standard architectures and work done by various researchers.
  arXiv  Detail & Related papers  (2021-11-05T05:37:10Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
  We compare the impact of different training procedures for diabetic retinopathy grading. We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
  arXiv  Detail & Related papers  (2021-06-25T08:32:45Z)
• Double Robust Representation Learning for Counterfactual Prediction [68.78210173955001]
  We propose a novel scalable method to learn double-robust representations for counterfactual predictions. We make robust and efficient counterfactual predictions for both individual and average treatment effects. The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.
  arXiv  Detail & Related papers  (2020-10-15T16:39:26Z)
• A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction [68.76594695163386]
  We propose a Deep Drift-Diffusion model inspired by psychologists to predict aesthetic score distribution from images. The DDD model can describe the psychological process of aesthetic perception instead of traditional modeling of the results of assessment. Our novel DDD model is simple but efficient, which outperforms the state-of-the-art methods in aesthetic score distribution prediction.
  arXiv  Detail & Related papers  (2020-10-15T11:01:46Z)
• MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement [17.038017337552724]
  We present a novel meta-learning approach called MetaPhys for personalized video-based cardiac measurement. Our method uses only 18-seconds of video for customization and works effectively in both supervised and unsupervised manners.
  arXiv  Detail & Related papers  (2020-10-05T04:41:03Z)
• 1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions [79.71065005161566]
  1-D convolutional neural network models are trained for classification of short-range sequences. Model provides prediction with high average accuracy on a hold out test set.
  arXiv  Detail & Related papers  (2020-02-06T17:25:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.