Internal Structure Attention Network for Fingerprint Presentation Attack Detection from Optical Coherence Tomography

• URL: http://arxiv.org/abs/2303.11034v1
• Date: Mon, 20 Mar 2023 11:36:09 GMT
• Title: Internal Structure Attention Network for Fingerprint Presentation Attack Detection from Optical Coherence Tomography
• Authors: Haohao Sun, Yilong Zhang, Peng Chen, Haixia Wang, Ronghua Liang
• Abstract summary: This paper presents a novel supervised learning-based PAD method, denoted as ISAPAD, which applies prior knowledge to guide network training. The proposed dual-branch architecture can not only learns global features from the OCT image, but also concentrate on layered structure feature. The simple yet effective ISAM enables the proposed network to obtain layered segmentation features belonging only to Bonafide from noisy OCT volume data.
• Score: 7.241363249424351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a non-invasive optical imaging technique, optical coherence tomography (OCT) has proven promising for automatic fingerprint recognition system (AFRS) applications. Diverse approaches have been proposed for OCT-based fingerprint presentation attack detection (PAD). However, considering the complexity and variety of PA samples, it is extremely challenging to increase the generalization ability with the limited PA dataset. To solve the challenge, this paper presents a novel supervised learning-based PAD method, denoted as ISAPAD, which applies prior knowledge to guide network training and enhance the generalization ability. The proposed dual-branch architecture can not only learns global features from the OCT image, but also concentrate on layered structure feature which comes from the internal structure attention module (ISAM). The simple yet effective ISAM enables the proposed network to obtain layered segmentation features belonging only to Bonafide from noisy OCT volume data directly. Combined with effective training strategies and PAD score generation rules, ISAPAD obtains optimal PAD performance in limited training data. Domain generalization experiments and visualization analysis validate the effectiveness of the proposed method for OCT PAD.

Related papers

• Cross-Phase Mutual Learning Framework for Pulmonary Embolism Identification on Non-Contrast CT Scans [7.732867194190985]
  Pulmonary embolism (PE) is a life-threatening condition where rapid and accurate diagnosis is imperative yet difficult due to predominantly atypical symptomatology. We propose a novel Cross-Phase Mutual learNing framework (CPMN) that fosters knowledge transfer from CTA to NCT scans. CPMN achieves the leading identification performance, which is 95.4% and 99.6% in patient-level sensitivity and specificity on NCT scans.
  arXiv  Detail & Related papers  (2024-07-16T09:29:33Z)
• Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped network [0.0]
  This paper introduces a novel approach to enhance the performance of pre-trained neural networks in medical image segmentation. We present the concept of Implantable Adaptive Cell (IAC), small but powerful modules identified through Partially-Connected DARTS. Our strategy allows for the seamless integration of the IAC into the pre-existing architecture, thereby enhancing its performance without necessitating a complete retraining from scratch.
  arXiv  Detail & Related papers  (2024-05-06T12:40:15Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Physics Inspired Hybrid Attention for SAR Target Recognition [61.01086031364307]
  We propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the issues. PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target. Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters.
  arXiv  Detail & Related papers  (2023-09-27T14:39:41Z)
• Impact of a DCT-driven Loss in Attention-based Knowledge-Distillation for Scene Recognition [64.29650787243443]
  We propose and analyse the use of a 2D frequency transform of the activation maps before transferring them. This strategy enhances knowledge transferability in tasks such as scene recognition. We publicly release the training and evaluation framework used along this paper at http://www.vpu.eps.uam.es/publications/DCTBasedKDForSceneRecognition.
  arXiv  Detail & Related papers  (2022-05-04T11:05:18Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing [42.733666815035534]
  Biometric systems are vulnerable to Presentation Attacks performed using various Presentation Attack Instruments (PAIs) We propose a self-supervised learning-based method, denoted as DF-DM. DF-DM is based on a global-local view coupled with De-Folding and De-Mixing to derive the task-specific representation for PAD.
  arXiv  Detail & Related papers  (2021-09-09T08:38:17Z)
• MPG-Net: Multi-Prediction Guided Network for Segmentation of Retinal Layers in OCT Images [11.370735571629602]
  We propose a novel multiprediction guided attention network (MPG-Net) for automated retinal layer segmentation in OCT images. MPG-Net consists of two major steps to strengthen the discriminative power of a U-shape Fully convolutional network (FCN) for reliable automated segmentation.
  arXiv  Detail & Related papers  (2020-09-28T21:22:22Z)
• A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation [33.684182783291064]
  Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people. The proposed framework consists of two models which provide reliable guidance for each other. Experiments on the publicly available AGE dataset demonstrate that the proposed framework outperforms the state-of-the-art semi-/weakly-supervised methods.
  arXiv  Detail & Related papers  (2020-07-20T11:26:32Z)
• Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting [86.33696045574692]
  We propose a Cycle Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for unsupervised nuclei segmentation in histopathology images. We first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images. Secondly, a semantic branch with a domain discriminator is designed to achieve panoptic-level domain adaptation.
  arXiv  Detail & Related papers  (2020-05-05T11:08:26Z)
• Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning [109.42987031347582]
  Face presentation attack detection (PAD) has been an urgent problem to be solved in the face recognition systems. We propose an efficient disentangled representation learning for cross-domain face PAD. Our approach consists of disentangled representation learning (DR-Net) and multi-domain learning (MD-Net)
  arXiv  Detail & Related papers  (2020-04-04T15:45:14Z)

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.