Simulation of prosthetic vision with PRIMA system and enhancement of face representation

• URL: http://arxiv.org/abs/2503.11677v2
• Date: Tue, 25 Mar 2025 21:46:32 GMT
• Title: Simulation of prosthetic vision with PRIMA system and enhancement of face representation
• Authors: Jungyeon Park, Anna Kochnev Goldstein, Yueming Zhuo, Nathan Jensen, Daniel Palanker,
• Abstract summary: This paper provides a novel, non-pixelated algorithm for simulating prosthetic vision.<n>It compares the algorithm's predictions to clinical perceptual outcomes.<n>It also offers computer vision and machine learning (ML) methods to improve face representation.
• Score: 3.607518121275142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objective. Patients implanted with the PRIMA photovoltaic subretinal prosthesis in geographic atrophy report form vision with the average acuity matching the 100um pixel size. Although this remarkable outcome enables them to read and write, they report difficulty with perceiving faces. This paper provides a novel, non-pixelated algorithm for simulating prosthetic vision the way it is experienced by PRIMA patients, compares the algorithm's predictions to clinical perceptual outcomes, and offers computer vision and machine learning (ML) methods to improve face representation. Approach. Our simulation algorithm integrates a grayscale filter, spatial resolution filter, and contrast filter. This accounts for the limited sampling density of the retinal implant, as well as the reduced contrast sensitivity of prosthetic vision. Patterns of Landolt C and faces created using this simulation algorithm are compared to reports from actual PRIMA users. To recover the facial features lost in prosthetic vision, we apply an ML facial landmarking model as well as contrast adjusting tone curves to the face image prior to its projection onto the implant. Main results. Simulated prosthetic vision matches the maximum letter acuity observed in clinical studies as well as patients' subjective descriptions. Application of the inversed contrast filter helps preserve the contrast in prosthetic vision. Identification of the facial features using an ML facial landmarking model and accentuating them further improve face representation. Significance. Spatial and contrast constraints of prosthetic vision limit resolvable features and degrade natural images. ML based methods and contrast adjustments mitigate some limitations and improve face representation. Even though higher spatial resolution can be expected with implants having smaller pixels, contrast enhancement still remains essential for face recognition.

Related papers

• OSDFace: One-Step Diffusion Model for Face Restoration [72.5045389847792]
  Diffusion models have demonstrated impressive performance in face restoration. We propose OSDFace, a novel one-step diffusion model for face restoration. Results demonstrate that OSDFace surpasses current state-of-the-art (SOTA) methods in both visual quality and quantitative metrics.
  arXiv  Detail & Related papers  (2024-11-26T07:07:48Z)
• CFCPalsy: Facial Image Synthesis with Cross-Fusion Cycle Diffusion Model for Facial Paralysis Individuals [3.2688425993442696]
  This study aims to synthesize a high-quality facial paralysis dataset to address this gap. A novel Cross-Fusion Cycle Palsy Expression Generative Model (PalsyCFC) based on the diffusion model is proposed. We have qualitatively and quantitatively evaluated the proposed method on the commonly used public clinical datasets of facial paralysis.
  arXiv  Detail & Related papers  (2024-09-11T13:46:35Z)
• Optimal-Landmark-Guided Image Blending for Face Morphing Attacks [8.024953195407502]
  We propose a novel approach for conducting face morphing attacks, which utilizes optimal-landmark-guided image blending. Our proposed method overcomes the limitations of previous approaches by optimizing the morphing landmarks and using Graph Convolutional Networks (GCNs) to combine landmark and appearance features.
  arXiv  Detail & Related papers  (2024-01-30T03:45:06Z)
• Unsupervised Anomaly Appraisal of Cleft Faces Using a StyleGAN2-based Model Adaptation Technique [5.224306534441244]
  This paper presents a novel machine learning framework to consistently detect, localize and rate congenital cleft lip anomalies in human faces. The proposed method employs the StyleGAN2 generative adversarial network with model adaptation to produce normalized transformations of cleft-affected faces. The anomaly scores yielded by the proposed computer model correlate closely with the human ratings of facial differences, leading to 0.942 Pearson's r score.
  arXiv  Detail & Related papers  (2022-11-12T13:30:20Z)
• Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers [57.1091606948826]
  We propose a novel FER model, named Poker Face Vision Transformer or PF-ViT, to address these challenges. PF-ViT aims to separate and recognize the disturbance-agnostic emotion from a static facial image via generating its corresponding poker face. PF-ViT utilizes vanilla Vision Transformers, and its components are pre-trained as Masked Autoencoders on a large facial expression dataset.
  arXiv  Detail & Related papers  (2022-07-22T13:39:06Z)
• Face Reconstruction with Variational Autoencoder and Face Masks [0.0]
  In this work, we investigated how face masks can help the training of VAEs for face reconstruction. An evaluation of the proposal using the celebA dataset shows that the reconstructed images are enhanced with the face masks.
  arXiv  Detail & Related papers  (2021-12-03T19:49:52Z)
• Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails [53.080403912727604]
  We propose a multi-stage Progressive Upsampling and Inpainting Generative Adversarial Network, dubbed Pro-UIGAN. It exploits facial geometry priors to replenish and upsample (8*) the occluded and tiny faces. Pro-UIGAN achieves visually pleasing HR faces, reaching superior performance in downstream tasks.
  arXiv  Detail & Related papers  (2021-08-02T02:29:24Z)
• Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection [65.92058628082322]
  Non-parametric face modeling aims to reconstruct 3D face only from images without shape assumptions. This paper presents a novel Learning to Aggregate and Personalize framework for unsupervised robust 3D face modeling.
  arXiv  Detail & Related papers  (2021-06-15T03:10:17Z)
• Inverting Generative Adversarial Renderer for Face Reconstruction [58.45125455811038]
  In this work, we introduce a novel Generative Adversa Renderer (GAR) GAR learns to model the complicated real-world image, instead of relying on the graphics rules, it is capable of producing realistic images. Our method achieves state-of-the-art performances on multiple face reconstruction.
  arXiv  Detail & Related papers  (2021-05-06T04:16:06Z)
• Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer [51.926868759681014]
  We present a framework to predict the facial parameters based on a bone-driven face model (BDFM) under different views. The proposed framework consists of a feature extractor, a generator, and a facial parameter regressor.
  arXiv  Detail & Related papers  (2020-08-20T09:49:13Z)

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.