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Super-resolution Imaging of Neuronal Dense-core Vesicles

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Triple-Type Feature Extraction for Palmprint Recognition.

Lian Wu¹, Yong Xu², Zhongwei Cui¹

¹School of Mathematics and Big Data, Guizhou Education University, Guiyang 550018, China.

Sensors (Basel, Switzerland)

|July 24, 2021

Summary

This summary is machine-generated.

This study introduces a novel heuristic palmprint recognition method that extracts texture, gradient, and direction features without needing training samples. This approach overcomes limitations of deep learning methods in palmprint recognition, offering effective identification with limited data.

Keywords:

biometrics matching score fusion palmprint recognition triple-type feature descriptors

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Spotting Cheetahs: Identifying Individuals by Their Footprints

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Published on: May 1, 2016

Area of Science:

Biometrics
Computer Vision
Pattern Recognition

Background:

Palmprint recognition is a user-friendly biometric technology due to its non-invasive and hygienic properties.
Current deep-learning methods for palmprint recognition require extensive labeled data, which is often difficult to obtain.
Limited availability of palmprint samples hinders the effectiveness of existing deep-learning-based recognition systems.

Purpose of the Study:

To propose a novel heuristic palmprint recognition method that does not require training samples.
To address the challenge of limited data availability in palmprint recognition systems.
To develop an effective palmprint recognition technique that utilizes inherent palmprint features.

Main Methods:

Extraction of three inherent palmprint features: texture, gradient, and direction.
Encoding these features into triple-type feature codes.
Representation using block-wise histograms of feature codes to form triple feature descriptors.
Weighted matching-score level fusion for similarity calculation and recognition.

Main Results:

The proposed heuristic method demonstrates promising effectiveness in palmprint recognition.
The method achieves good performance without the need for any training samples.
Experimental results on three widely used palmprint databases validate the method's efficacy.

Conclusions:

The developed heuristic palmprint recognition method is effective, particularly in scenarios with limited data.
The triple-type feature extraction and fusion strategy provide a robust approach to palmprint identification.
This method offers a viable alternative to data-intensive deep-learning techniques for palmprint recognition.