Experimental results on palmvein-based personal recognition by multi-snapshot fusion of textural features

• URL: http://arxiv.org/abs/2008.00821v2
• Date: Sat, 17 Oct 2020 12:00:52 GMT
• Title: Experimental results on palmvein-based personal recognition by multi-snapshot fusion of textural features
• Authors: Mohanad Abukmeil and Gian Luca Marcialis
• Abstract summary: We investigate multiple snapshot fusion of textural features for palmvein recognition including identification and verification. Our goal in this paper is to show that this is confirmed for palmvein recognition, thus allowing to achieve very high recognition rates on a well-known benchmark data set.
• Score: 3.274290296343038
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we investigate multiple snapshot fusion of textural features for palmvein recognition including identification and verification. Although the literature proposed several approaches for palmvein recognition, the palmvein performance is still affected by identification and verification errors. As well-known, palmveins are usually described by line-based methods which enhance the vein flow. This is claimed to be unique from person to person. However, palmvein images are also characterized by texture that can be pointed out by textural features, which relies on recent and efficient hand-crafted algorithms such as Local Binary Patterns, Local Phase Quantization, Local Tera Pattern, Local directional Pattern, and Binarized Statistical Image Features (LBP, LPQ, LTP, LDP and BSIF, respectively), among others. Finally, they can be easily managed at feature-level fusion, when more than one sample can be acquired for recognition. Therefore, multi-snapshot fusion can be adopted for exploiting these features complementarity. Our goal in this paper is to show that this is confirmed for palmvein recognition, thus allowing to achieve very high recognition rates on a well-known benchmark data set.

Related papers

• Cross-Chirality Palmprint Verification: Left is Right for the Right Palmprint [11.388567430575783]
  This paper introduces a novel Cross-Chirality Palmprint Verification (CCPV) framework that challenges the conventional wisdom in traditional palmprint verification systems. Unlike existing methods that typically require storing both left and right palmprints, our approach enables verification using either palm while storing only one palmprint template.
  arXiv  Detail & Related papers  (2024-09-19T19:10:21Z)
• GenPalm: Contactless Palmprint Generation with Diffusion Models [25.738682467090335]
  This paper introduces a novel palmprint generation method using diffusion probabilistic models. We develop an end-to-end framework for synthesizing multiple palm identities and validate the realism and utility of the generated palmprints.
  arXiv  Detail & Related papers  (2024-06-01T03:33:25Z)
• Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition [1.534667887016089]
  We propose a fusion based local matching approach towards latent fingerprint recognition. Proposed approach would integrate these handcrafted features with a recently proposed deep neural network embedding features in a multi-stage fusion approach.
  arXiv  Detail & Related papers  (2024-03-24T14:29:41Z)
• Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification [47.35171881187345]
  We propose a physics-driven spectrum-consistent federated learning method for palmprint verification, dubbed as PSFed-Palm. Our approach first partitions clients into short- and long-spectrum groups according to the wavelength range of their local spectrum images. We impose constraints on the local models to ensure their consistency with the global model, effectively preventing model drift.
  arXiv  Detail & Related papers  (2023-08-01T11:01:17Z)
• Exploring Incompatible Knowledge Transfer in Few-shot Image Generation [107.81232567861117]
  Few-shot image generation learns to generate diverse and high-fidelity images from a target domain using a few reference samples. Existing F SIG methods select, preserve and transfer prior knowledge from a source generator to learn the target generator. We propose knowledge truncation, which is a complementary operation to knowledge preservation and is implemented by a lightweight pruning-based method.
  arXiv  Detail & Related papers  (2023-04-15T14:57:15Z)
• Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images [60.34381768479834]
  Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. We pioneer a systematic study on deepfake detection generated by state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-04-02T10:25:09Z)
• Advancing 3D finger knuckle recognition via deep feature learning [51.871256510747465]
  Contactless 3D finger knuckle patterns have emerged as an effective biometric identifier due to its discriminativeness, visibility from a distance, and convenience. Recent research has developed a deep feature collaboration network which simultaneously incorporates intermediate features from deep neural networks with multiple scales. This paper advances this approach by investigating the possibility of learning a discriminative feature vector with the least possible dimension for representing 3D finger knuckle images.
  arXiv  Detail & Related papers  (2023-01-07T20:55:16Z)
• Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification [56.128200319868526]
  Signal-quality awareness has been found to increase recognition rates and to support decisions in multisensor environments significantly. Here, we study the orientation tensor of fingerprint images to quantify signal impairments, such as noise, lack of structure, blur, with the help of symmetry descriptors. The quantitative results favor quality awareness under all aspects, boosting recognition rates and fusing differently skilled experts efficiently as well as effectively.
  arXiv  Detail & Related papers  (2022-11-24T12:17:49Z)
• Compact multi-scale periocular recognition using SAFE features [63.48764893706088]
  We present a new approach for periocular recognition based on the Symmetry Assessment by Feature Expansion (SAFE) descriptor. We use the sclera center as single key point for feature extraction, highlighting the object-like identity properties that concentrates to this point unique of the eye.
  arXiv  Detail & Related papers  (2022-10-18T11:46:38Z)
• BPFNet: A Unified Framework for Bimodal Palmprint Alignment and Fusion [26.770714818092774]
  Bimodal palmprint recognition leverages palmprint and palm vein images simultaneously. In this paper, we propose Bimodal Palmprint Fusion Network (BPFNet) which focuses on ROI localization, alignment and bimodal image fusion.
  arXiv  Detail & Related papers  (2021-10-04T04:30:36Z)
• N-shot Palm Vein Verification Using Siamese Networks [1.0312968200748116]
  This paper proposes an architecture using Siamese neural network structure for few shot palm vein identification. The architecture performance was tested on the HK PolyU multi spectral palm vein database with limited samples. The results suggest that the method is effective since it has 91.9% precision, 91.1% recall, 92.2% specificity, 91.5%, F1-Score, and 90.5% accuracy values.
  arXiv  Detail & Related papers  (2021-09-27T05:47:54Z)

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.