Towards Synthetic Data Generation for Improved Pain Recognition in Videos under Patient Constraints

• URL: http://arxiv.org/abs/2409.16382v1
• Date: Tue, 24 Sep 2024 18:33:57 GMT
• Title: Towards Synthetic Data Generation for Improved Pain Recognition in Videos under Patient Constraints
• Authors: Jonas Nasimzada, Jens Kleesiek, Ken Herrmann, Alina Roitberg, Constantin Seibold,
• Abstract summary: This study introduces a novel approach that leverages synthetic data to enhance video-based pain recognition models. We present a pipeline that synthesizes realistic 3D facial models by capturing nuanced facial movements from a small participant pool. This process generates 8,600 synthetic faces, accurately reflecting genuine pain expressions from varied angles and perspectives.
• Score: 11.515273901289472
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recognizing pain in video is crucial for improving patient-computer interaction systems, yet traditional data collection in this domain raises significant ethical and logistical challenges. This study introduces a novel approach that leverages synthetic data to enhance video-based pain recognition models, providing an ethical and scalable alternative. We present a pipeline that synthesizes realistic 3D facial models by capturing nuanced facial movements from a small participant pool, and mapping these onto diverse synthetic avatars. This process generates 8,600 synthetic faces, accurately reflecting genuine pain expressions from varied angles and perspectives. Utilizing advanced facial capture techniques, and leveraging public datasets like CelebV-HQ and FFHQ-UV for demographic diversity, our new synthetic dataset significantly enhances model training while ensuring privacy by anonymizing identities through facial replacements. Experimental results demonstrate that models trained on combinations of synthetic data paired with a small amount of real participants achieve superior performance in pain recognition, effectively bridging the gap between synthetic simulations and real-world applications. Our approach addresses data scarcity and ethical concerns, offering a new solution for pain detection and opening new avenues for research in privacy-preserving dataset generation. All resources are publicly available to encourage further innovation in this field.

Related papers

• Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attacks [5.0243930429558885]
  This paper introduces Knowledge Recycling (KR), a pipeline designed to optimise the generation and use of synthetic data for training downstream classifiers. At the heart of this pipeline is Generative Knowledge Distillation (GKD), the proposed technique that significantly improves the quality and usefulness of the information. The results show a significant reduction in the performance gap between models trained on real and synthetic data, with models based on synthetic data outperforming those trained on real data in some cases.
  arXiv  Detail & Related papers  (2024-07-22T10:31:07Z)
• Image Distillation for Safe Data Sharing in Histopathology [10.398266052019675]
  Histopathology can help clinicians make accurate diagnoses, determine disease prognosis, and plan appropriate treatment strategies. As deep learning techniques prove successful in the medical domain, the primary challenges become limited data availability and concerns about data sharing and privacy. We create a small synthetic dataset that encapsulates essential information, which can be shared without constraints. We train a latent diffusion model and construct a new distilled synthetic dataset with a small number of human readable synthetic images.
  arXiv  Detail & Related papers  (2024-06-19T13:19:08Z)
• Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data [104.45155847778584]
  This paper presents an overview of the 2nd edition of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) FRCSyn aims to investigate the use of synthetic data in face recognition to address current technological limitations.
  arXiv  Detail & Related papers  (2024-04-16T08:15:10Z)
• Best Practices and Lessons Learned on Synthetic Data [83.63271573197026]
  The success of AI models relies on the availability of large, diverse, and high-quality datasets. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns.
  arXiv  Detail & Related papers  (2024-04-11T06:34:17Z)
• SDFR: Synthetic Data for Face Recognition Competition [51.9134406629509]
  Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. Recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones.
  arXiv  Detail & Related papers  (2024-04-06T10:30:31Z)
• Zero-shot and Few-shot Generation Strategies for Artificial Clinical Records [1.338174941551702]
  This study assesses the capability of the Llama 2 LLM to create synthetic medical records that accurately reflect real patient information. We focus on generating synthetic narratives for the History of Present Illness section, utilising data from the MIMIC-IV dataset for comparison. Our findings suggest that this chain-of-thought prompted approach allows the zero-shot model to achieve results on par with those of fine-tuned models, based on Rouge metrics evaluation.
  arXiv  Detail & Related papers  (2024-03-13T16:17:09Z)
• Learning Human Action Recognition Representations Without Real Humans [66.61527869763819]
  We present a benchmark that leverages real-world videos with humans removed and synthetic data containing virtual humans to pre-train a model. We then evaluate the transferability of the representation learned on this data to a diverse set of downstream action recognition benchmarks. Our approach outperforms previous baselines by up to 5%.
  arXiv  Detail & Related papers  (2023-11-10T18:38:14Z)
• Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data [11.31971398273479]
  We propose measures to add real-world noise to synthetic physiological signals and corresponding facial videos. Our results show that we were able to reduce the average MAE from 6.9 to 2.0.
  arXiv  Detail & Related papers  (2023-11-09T13:55:45Z)
• SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data [8.026313049094146]
  State-of-the-art face recognition networks are often computationally expensive and cannot be used for mobile applications. We propose a new framework to train lightweight face recognition models by distilling the knowledge of a pretrained teacher face recognition model using synthetic data. We use synthetic face images without identity labels, mitigating the problems in the intra-class variation generation of synthetic datasets.
  arXiv  Detail & Related papers  (2023-08-28T19:15:27Z)
• Is synthetic data from generative models ready for image recognition? [69.42645602062024]
  We study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks.
  arXiv  Detail & Related papers  (2022-10-14T06:54:24Z)
• SynFace: Face Recognition with Synthetic Data [83.15838126703719]
  We devise the SynFace with identity mixup (IM) and domain mixup (DM) to mitigate the performance gap. We also perform a systematically empirical analysis on synthetic face images to provide some insights on how to effectively utilize synthetic data for face recognition.
  arXiv  Detail & Related papers  (2021-08-18T03:41:54Z)

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.