Human Recognition Using Face in Computed Tomography

• URL: http://arxiv.org/abs/2005.14238v1
• Date: Thu, 28 May 2020 18:59:59 GMT
• Title: Human Recognition Using Face in Computed Tomography
• Authors: Jiuwen Zhu, Hu Han, and S. Kevin Zhou
• Abstract summary: We propose an automatic processing pipeline that first detects facial landmarks in 3D for ROI extraction and then generates aligned 2D depth images, which are used for automatic recognition. Our method achieves a 1:56 identification accuracy of 92.53% and a 1:1 verification accuracy of 96.12%, outperforming other competing approaches.
• Score: 26.435782518817295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the mushrooming use of computed tomography (CT) images in clinical decision making, management of CT data becomes increasingly difficult. From the patient identification perspective, using the standard DICOM tag to track patient information is challenged by issues such as misspelling, lost file, site variation, etc. In this paper, we explore the feasibility of leveraging the faces in 3D CT images as biometric features. Specifically, we propose an automatic processing pipeline that first detects facial landmarks in 3D for ROI extraction and then generates aligned 2D depth images, which are used for automatic recognition. To boost the recognition performance, we employ transfer learning to reduce the data sparsity issue and to introduce a group sampling strategy to increase inter-class discrimination when training the recognition network. Our proposed method is capable of capturing underlying identity characteristics in medical images while reducing memory consumption. To test its effectiveness, we curate 600 3D CT images of 280 patients from multiple sources for performance evaluation. Experimental results demonstrate that our method achieves a 1:56 identification accuracy of 92.53% and a 1:1 verification accuracy of 96.12%, outperforming other competing approaches.

Related papers

• mmID: High-Resolution mmWave Imaging for Human Identification [16.01613518230451]
  This paper proposes to improve imaging resolution by estimating the human figure as a whole using conditional generative adversarial networks (cGAN) Our system generates environmentally independent, high-resolution images that can extract unique physical features useful for human identification. Our finding indicates high-resolution accuracy with 5% mean silhouette difference to the Kinect device.
  arXiv  Detail & Related papers  (2024-02-01T20:19:38Z)
• Diffusion Facial Forgery Detection [56.69763252655695]
  This paper introduces DiFF, a comprehensive dataset dedicated to face-focused diffusion-generated images. We conduct extensive experiments on the DiFF dataset via a human test and several representative forgery detection methods. The results demonstrate that the binary detection accuracy of both human observers and automated detectors often falls below 30%.
  arXiv  Detail & Related papers  (2024-01-29T03:20:19Z)
• Swin-Tempo: Temporal-Aware Lung Nodule Detection in CT Scans as Video Sequences Using Swin Transformer-Enhanced UNet [2.7547288571938795]
  We present an innovative model that harnesses the strengths of both convolutional neural networks and vision transformers. Inspired by object detection in videos, we treat each 3D CT image as a video, individual slices as frames, and lung nodules as objects, enabling a time-series application.
  arXiv  Detail & Related papers  (2023-10-05T07:48:55Z)
• Towards Head Computed Tomography Image Reconstruction Standardization with Deep Learning Assisted Automatic Detection [5.288684776927016]
  Three-dimensional (3D) reconstruction of head Computed Tomography (CT) images elucidates the intricate spatial relationships of tissue structures. securing an optimal head CT scan without deviation is challenging in clinical settings, owing to poor positioning by technicians, patient's physical constraints, or CT scanner tilt angle restrictions. We propose an efficient automatic head CT images 3D reconstruction method, improving accuracy and repeatability, as well as diminishing manual intervention.
  arXiv  Detail & Related papers  (2023-07-31T06:58:49Z)
• Multi-View Vertebra Localization and Identification from CT Images [57.56509107412658]
  We propose a multi-view vertebra localization and identification from CT images. We convert the 3D problem into a 2D localization and identification task on different views. Our method can learn the multi-view global information naturally.
  arXiv  Detail & Related papers  (2023-07-24T14:43:07Z)
• Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks [0.0]
  We propose an automatic algorithm aimed at volumetric segmentation of acute ischemic stroke lesion in non-contrast computed tomography brain 3D images. Our deep-learning approach is based on the popular 3D U-Net convolutional neural network architecture.
  arXiv  Detail & Related papers  (2023-01-17T10:39:39Z)
• Mediastinal Lymph Node Detection and Segmentation Using Deep Learning [1.7188280334580195]
  In clinical practice, computed tomography (CT) and positron emission tomography (PET) imaging detect abnormal lymph nodes (LNs) Deep convolutional neural networks frequently segment items in medical photographs. A well-established deep learning technique UNet was modified using bilinear and total generalized variation (TGV) based up strategy to segment and detect mediastinal lymph nodes. The modified UNet maintains texture discontinuities, selects noisy areas, searches appropriate balance points through backpropagation, and recreates image resolution.
  arXiv  Detail & Related papers  (2022-11-24T02:55:20Z)
• Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
  We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
  arXiv  Detail & Related papers  (2022-09-19T09:43:19Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• 3D Convolutional Neural Networks for Stalled Brain Capillary Detection [72.21315180830733]
  Brain vasculature dysfunctions such as stalled blood flow in cerebral capillaries are associated with cognitive decline and pathogenesis in Alzheimer's disease. Here, we describe a deep learning-based approach for automatic detection of stalled capillaries in brain images based on 3D convolutional neural networks. In this setting, our approach outperformed other methods and demonstrated state-of-the-art results, achieving 0.85 Matthews correlation coefficient, 85% sensitivity, and 99.3% specificity.
  arXiv  Detail & Related papers  (2021-04-04T20:30:14Z)
• Joint Deep Learning of Facial Expression Synthesis and Recognition [97.19528464266824]
  We propose a novel joint deep learning of facial expression synthesis and recognition method for effective FER. The proposed method involves a two-stage learning procedure. Firstly, a facial expression synthesis generative adversarial network (FESGAN) is pre-trained to generate facial images with different facial expressions. In order to alleviate the problem of data bias between the real images and the synthetic images, we propose an intra-class loss with a novel real data-guided back-propagation (RDBP) algorithm.
  arXiv  Detail & Related papers  (2020-02-06T10:56:00Z)

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.