Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

IR Frequency Region: Fingerprint Region

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Autophagy modulation in ulcerative colitis with traditional Chinese medicine: A review.

Pathology, research and practice·2026

Same author

Inotodiol ameliorates oxidative stress and apoptosis by regulating PI3K/Akt/GSK-3β signaling pathways in diabetic nephropathy.

Renal failure·2026

Same author

Effects of traditional Chinese medicine nursing techniques combined with music-based interventions on sleep in older adults: a systematic review and meta-analysis.

Frontiers in psychiatry·2026

Same author

Structural Regulation, Photothermal Conversion, and Interfacial Heat Transfer Mechanisms of Silver Nanoparticle/Wood-Derived Porous Carbon Composite Phase Change Materials.

Nanomaterials (Basel, Switzerland)·2026

Same author

Natural variation of <i>CTS1</i> confers cold tolerance and blast resistance in rice.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Non-Contact IOP Estimation Based on Corneal Stress Birefringence: Experimental and Computational Validation.

Sensors (Basel, Switzerland)·2026

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 21, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Multispectral palmprint recognition using a quaternion matrix.

Xingpeng Xu¹, Zhenhua Guo², Changjiang Song³

¹Bio-Computing Research Center, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China.

Sensors (Basel, Switzerland)

|June 6, 2012

Summary

This summary is machine-generated.

This study introduces a novel quaternion model for multispectral palmprint recognition, enhancing accuracy by fully utilizing spectral information. The new method achieved a high recognition rate of 98.83% in experiments.

Keywords:

DWT PCA multispectral palmprints quaternion

More Related Videos

Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation (BiFC-PALM)

Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation (BiFC-PALM)

Published on: December 22, 2015

Related Experiment Videos

Last Updated: May 21, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation (BiFC-PALM)

Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation (BiFC-PALM)

Published on: December 22, 2015

Area of Science:

Biometrics
Computer Vision
Pattern Recognition

Background:

Palmprint recognition is a long-standing biometric modality.
Traditional methods use white light, while multispectral imaging offers higher accuracy.
Existing multispectral approaches suffer information loss during fusion.

Purpose of the Study:

To propose a novel quaternion model for multispectral palmprint recognition.
To fully leverage multispectral information without loss.
To improve the accuracy of palmprint-based biometric systems.

Main Methods:

Representing multispectral palmprint images (red, green, blue, NIR) using a quaternion matrix.
Applying Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) for feature extraction.
Utilizing Euclidean distance for feature dissimilarity measurement and a nearest neighborhood classifier for decision.

Main Results:

The quaternion model effectively utilizes multispectral information.
Achieved a recognition rate of 98.83% with 3000 test samples from 500 palms.
Demonstrated superior performance compared to traditional fusion methods.

Conclusions:

The proposed quaternion-based method significantly enhances multispectral palmprint recognition accuracy.
This approach offers a more effective way to process and utilize rich spectral data.
The findings support the adoption of quaternion models in advanced biometric systems.