Abstract. Palm vein biometric technology is widely regarded as highly secure due to its challenging-to-forge characteristics. However, recent empirical studies have revealed that forged vein patterns printed on paper can deceive palm vein recognition systems, thereby leading to security breaches. The conventional approach to address this issue involves performing liveness detection followed by preprocessing the palm vein image prior to recognition, which increases the algorithmic complexity and might adversely affect overall performance. To overcome these limitations, we propose a multibranch network (PVCodeNet++) for end-to-end integration of palm vein recognition and liveness detection using a multitask learning approach. Specifically, our proposed model leverages network weight sharing and mutual assistance between network branches to enhance overall performance. We utilize the transformer encoder as the underlying shared component, employ central difference convolution for the liveness detection branch, introduce the normalized attention mechanism, and balance the multitask loss through the uncertainty weighting method. Experiments on palm vein liveness and spoofing datasets show that the proposed PVCodeNet++ has an equal error rate of 0 for recognition performance metrics on various datasets, a significant improvement in the intraclass compactness and interclass separability separation metric, increasing from 7.88 to 9.37 on the PolyU dataset; and an average classification error rate of 0 for liveness detection performance metrics, demonstrating the feasibility and effectiveness of the method proposed.

End-to-end multibranch network for palm vein recognition and liveness detection

In recent years, palmprints have been widely used for individual verification. The rich privacy information in palmprint data necessitates its protection to ensure security and privacy without sacrificing system performance. Existing systems often use cancelable technologies to protect templates, but these technologies ignore the potential risk of data leakage. Upon breaching the system and gaining access to the stored database, a hacker could easily manipulate the stored templates, compromising the security of the verification system. To address this issue, we propose a dual-level cancelable palmprint verification framework in this paper. Specifically, the raw template is initially encrypted using a competition hashing network with a first-level token, facilitating the end-to-end generation of cancelable templates. Different from previous works, the protected template undergoes further encryption to differentiate the second-level protected template from the first-level one. The system specifically creates a negative database (NDB) with the second-level token for dual-level protection during the enrollment stage. Reversing the NDB is NP-hard and a fine-grained algorithm for NDB generation is introduced to manage the noise and specified bits. During the verification stage, we propose an NDB matching algorithm based on matrix operation to accelerate the matching process of previous NDB methods caused by dictionary-based matching rules. This approach circumvents the need to store templates identical to those utilized for verification, reducing the risk of potential data leakage. Extensive experiments conducted on public palmprint datasets have confirmed the effectiveness and generality of the proposed framework. Upon acceptance of the paper, the code will be accessible at https://github.com/Deep-Imaging-Group/NPR.

A Dual-Level Cancelable Framework for Palmprint Verification and Hack-Proof Data Storage

A novel method based on the cross‐modality intersecting features of the palm‐vein and the palmprint is proposed for identity verification. Capitalising on the unique geometrical relationship between the two biometric modalities, the cross‐modality intersecting points provides a stable set of features for identity verification. To facilitate flexibility in template changes, a template transformation is proposed. While maintaining non‐invertibility, the template transformation allows transformation sizes beyond that offered by the conventional means. Extensive experiments using three public palm databases are conducted to verify the effectiveness the proposed system for identity recognition.

Extraction of intersecting palm‐vein and palmprint features for cancellable identity verification

In this paper, we propose a system for user identification based on palm-veins extracted from multi-spectral images of the palm. Essentially, a feature level fusion is firstly conducted by stacking the preprocessed palm images from multiple image spectrums to increase the richness of information. Subsequently, a convolution neural network (CNN), which utilizes the residual learning with a linear bottleneck scheme, is adopted to learn the stacked features. The proposed system has been evaluated on a public multispectral palm database where a promising performance in terms of the identification accuracy has been observed.

Multispectral Palm-vein Fusion for User Identification

Wi-Fi Based Human Activity Recognition Using BiLSTM with Kernel Ridge Regression

In this paper, we propose to extract the intersection points of the palmprint and the palm-vein lines from multi-spectral images and use them as reliable features for identity verification. Essentially, by utilizing a sum of cardinal directional image difference operation, the palmprint and palm-vein line features are respectively extracted from palm images of the Blue channel and the NIR channel of image spectrums based on simple matrix projection. Subsequently, the intersection locations of the two biometric line features are extracted and utilized to compute a set of keypoint descriptors. After calculating the match scores based on the extracted keypoint descriptors, a score level fusion of the matching results obtained from the Blue channel and the NIR channel is adopted to enhance the verification performance. The proposed method has been experimented on a public domain multispectral palm database where encouraging results in terms of verification accuracy have been obtained.

Identity Verification based on the RGB and NIR Images of the Palm

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jaekwon Lee

Author Tools

Chat about Author

Papers

Extraction of intersecting palm‐vein and palmprint features for cancellable identity verification

Multispectral Palm-vein Fusion for User Identification

Wi-Fi Based Human Activity Recognition Using BiLSTM with Kernel Ridge Regression

Identity Verification based on the RGB and NIR Images of the Palm