Face Reconstruction with Variational Autoencoder and Face Masks

• URL: http://arxiv.org/abs/2112.02139v1
• Date: Fri, 3 Dec 2021 19:49:52 GMT
• Title: Face Reconstruction with Variational Autoencoder and Face Masks
• Authors: Rafael S. Toledo, Eric A. Antonelo
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Variational AutoEncoders (VAE) employ deep learning models to learn a continuous latent z-space that is subjacent to a high-dimensional observed dataset. With that, many tasks are made possible, including face reconstruction and face synthesis. In this work, we investigated how face masks can help the training of VAEs for face reconstruction, by restricting the learning to the pixels selected by the face mask. An evaluation of the proposal using the celebA dataset shows that the reconstructed images are enhanced with the face masks, especially when SSIM loss is used either with l1 or l2 loss functions. We noticed that the inclusion of a decoder for face mask prediction in the architecture affected the performance for l1 or l2 loss functions, while this was not the case for the SSIM loss. Besides, SSIM perceptual loss yielded the crispest samples between all hypotheses tested, although it shifts the original color of the image, making the usage of the l1 or l2 losses together with SSIM helpful to solve this issue.

Related papers

• Seeing through the Mask: Multi-task Generative Mask Decoupling Face Recognition [47.248075664420874]
  Current general face recognition system suffers from serious performance degradation when encountering occluded scenes. This paper proposes a Multi-task gEnerative mask dEcoupling face Recognition (MEER) network to jointly handle these two tasks. We first present a novel mask decoupling module to disentangle mask and identity information, which makes the network obtain purer identity features from visible facial components.
  arXiv  Detail & Related papers  (2023-11-20T03:23:03Z)
• Masked Student Dataset of Expressions [1.52292571922932]
  We present a novel dataset consisting of 1,960 real-world non-masked and masked facial expression images collected from 142 individuals. Along with the issue of obfuscated facial features, we illustrate how other subtler issues in masked FER are represented in our dataset. To tackle this, we test two training paradigms: contrastive learning and knowledge distillation, and find that they increase the model's performance in the masked scenario while maintaining its non-masked performance.
  arXiv  Detail & Related papers  (2023-04-07T23:43:21Z)
• Learning Representations for Masked Facial Recovery [8.124282476398843]
  pandemic of these recent years has led to a dramatic increase in people wearing protective masks in public venues. One way to address the problem is to revert to face recovery methods as a preprocessing step. We introduce a method that is specific for the recovery of the face image from an image of the same individual wearing a mask.
  arXiv  Detail & Related papers  (2022-12-28T22:22:15Z)
• FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders [81.21440457805932]
  We propose a novel framework FaceMAE, where the face privacy and recognition performance are considered simultaneously. randomly masked face images are used to train the reconstruction module in FaceMAE. We also perform sufficient privacy-preserving face recognition on several public face datasets.
  arXiv  Detail & Related papers  (2022-05-23T07:19:42Z)
• Mask-invariant Face Recognition through Template-level Knowledge Distillation [3.727773051465455]
  Masks affect the performance of previous face recognition systems. We propose a mask-invariant face recognition solution (MaskInv) In addition to the distilled knowledge, the student network benefits from additional guidance by margin-based identity classification loss.
  arXiv  Detail & Related papers  (2021-12-10T16:19:28Z)
• End2End Occluded Face Recognition by Masking Corrupted Features [82.27588990277192]
  State-of-the-art general face recognition models do not generalize well to occluded face images. This paper presents a novel face recognition method that is robust to occlusions based on a single end-to-end deep neural network. Our approach, named FROM (Face Recognition with Occlusion Masks), learns to discover the corrupted features from the deep convolutional neural networks, and clean them by the dynamically learned masks.
  arXiv  Detail & Related papers  (2021-08-21T09:08:41Z)
• Network Architecture Search for Face Enhancement [82.25775020564654]
  We present a multi-task face restoration network, called Network Architecture Search for Face Enhancement (NASFE) NASFE can enhance poor quality face images containing a single degradation (i.e. noise or blur) or multiple degradations (noise+blur+low-light)
  arXiv  Detail & Related papers  (2021-05-13T19:46:05Z)
• Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness [66.55719330810547]
  Inpainting arbitrary missing regions is challenging because learning valid features for various masked regions is nontrivial. We propose a novel mask-aware inpainting solution that learns multi-scale features for missing regions in the encoding phase. Our framework is validated both quantitatively and qualitatively via extensive experiments on three public datasets.
  arXiv  Detail & Related papers  (2021-04-28T13:17:47Z)
• Towards NIR-VIS Masked Face Recognition [47.00916333095693]
  Near-infrared to visible (NIR-VIS) face recognition is the most common case in heterogeneous face recognition. We propose a novel training method to maximize the mutual information shared by the face representation of two domains. In addition, a 3D face reconstruction based approach is employed to synthesize masked face from the existing NIR image.
  arXiv  Detail & Related papers  (2021-04-14T10:40:09Z)
• Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection [103.7264459186552]
  Face presentation attack detection (PAD) is essential to secure face recognition systems. Most existing 3D mask PAD benchmarks suffer from several drawbacks. We introduce a largescale High-Fidelity Mask dataset to bridge the gap to real-world applications.
  arXiv  Detail & Related papers  (2021-04-13T12:48:38Z)

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.