余弦相似度保持的掌纹识别算法

丘展春; 费伦科; 滕少华; 张巍

doi:10.12052/gdutxb.210023

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余弦相似度保持的掌纹识别算法

更新时间：2026-05-27

- 余弦相似度保持的掌纹识别算法
- Palmprint Recognition Based on Cosine Similarity
- 广东工业大学学报 2022年39卷第3期页码：55-62
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  丘展春(1995–)，男，硕士研究生，主要研究方向为掌纹识别
  费伦科(1982–)，男，副教授，博士，主要研究方向为机器学习和生物特征识别，E-mail：flksxm@126.com
- 基金信息：
  
  国家自然科学基金资助项目(62176066，61972102);广东省自然科学基金资助项目(2019A1515011811);广东省重点研发计划项目(2020B010166006);广州市科技计划项目(202002030110)
- DOI：10.12052/gdutxb.210023
  中图分类号： TP391.4;TP183
- 网络首发：2022-05-10，
  
  纸质出版：2022
- 稿件说明：
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丘展春, 费伦科, 滕少华, 张巍. 余弦相似度保持的掌纹识别算法[J]. 广东工业大学学报, 2022, 39(3): 55-62.

Qiu Zhan-chun, Fei Lun-ke, Teng Shao-hua, et al. Palmprint Recognition Based on Cosine Similarity[J]. 2022, 39(3): 55-62.
丘展春, 费伦科, 滕少华, 张巍. 余弦相似度保持的掌纹识别算法[J]. 广东工业大学学报, 2022, 39(3): 55-62. DOI： 10.12052/gdutxb.210023.

Qiu Zhan-chun, Fei Lun-ke, Teng Shao-hua, et al. Palmprint Recognition Based on Cosine Similarity[J]. 2022, 39(3): 55-62. DOI： 10.12052/gdutxb.210023.

摘要

掌纹识别作为一种新兴的生物特征识别技术，具有识别率高、特征稳定等优点。传统的基于手工提取特征的掌纹识别算法使用先验知识提取掌纹主线和细节点，存在可扩展性低、提取图像特征困难、无法挖掘数据的隐藏信息等问题。为了解决这些问题，本文提出了一种基于学习的掌纹识别算法。首先提取掌纹图像的像素值差向量(Pixel Different Vector，PDV)特征。然后，通过余弦相似度保持模型，同时学习PDV特征的二进制表示及其映射函数，以减少PDV特征的信息冗余。最后，为了处理掌纹位置偏移和光照变化等噪音，将学习得到的二进制特征编码成直方图描述子。在3个广泛使用的掌纹数据库上的实验结果表明，所提出的算法能更好地挖掘掌纹图像的内在特征，有效地提高掌纹识别精度。

Abstract

Palmprint recognition

as a promising biometric recognition technology

has the advantages of high recognition accuracy and stable characteristics. Conventional hand-craft based palmprint recognition methods extract the principal lines and minutiae points depending on prior knowledge

which suffer from various limitations

such as the difficulties of scalability

feature extraction and information learning. To address this

a learning-based palmprint recognition method is proposed. Specifically

the Pixel Different Feature (PDV) feature is first extracted from palmprint image. Then

the binary representation and projection function based on PDV features is simultaneously learned via a cosine similarity preserving model. To alleviate the effects of noise such as palmprint position shifting and illumination changing

the binary representation is finally converted into a histogram descriptor. The experimental results on three widely used palmprint databases demonstrate that the proposed method can better excavate the intrinsic structure of palmprint images and significantly improve the performance of palmprint recognition.

关键词

Keywords

references

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