﻿ 基于LDP和PNN的掌纹识别算法 Research of Palmprint Identification Algorithm Based on LDP and PNN

Computer Science and Application
Vol.08 No.04(2018), Article ID:24504,8 pages
10.12677/CSA.2018.84051

Research of Palmprint Identification Algorithm Based on LDP and PNN

Pingping Zhou, Xuan Wang

School of Physics and Information Technology, Shaanxi Normal University, Xi’an Shaanxi

Received: Apr. 2nd, 2018; accepted: Apr. 18th, 2018; published: Apr. 25th, 2018

ABSTRACT

To alleviate the limitations that the existing palmprint recognition methods are time-consuming, and their robustness to the variations of orientation, position and illumination is insufficient, this paper uses LDP operator to get feature extraction. The paimprint image is divided into sub-regions. Then connecting these sub-regions LDP histogram to generate palmprint feature vector, this can increase the similarity of the same type of palmprint image. In order to improve the recognition accuracy and accelerate the recognition speed, the classification is performed by Probabilistic Neural Networks (PNN). It is also shown that the proposed approach is robust to the variations of orientation, position and illumination and improves the recognition rate, and accelerates the recognition speed.

Keywords:Feature Vector, LDP Histogram, Probabilistic Neural Networks (PNN), Palmprint Recognition

Copyright © 2018 by authors and Hans Publishers Inc.

This work is licensed under the Creative Commons Attribution International License (CC BY).

http://creativecommons.org/licenses/by/4.0/

1. 引言

2. 基于LDP的特征提取

2.1. LDP描述子

${M}_{0}=\left[\begin{array}{ccc}-3& -3& 5\\ -3& 0& 5\\ -3& -3& 5\end{array}\right]$ , ${M}_{1}=\left[\begin{array}{ccc}-3& 5& 5\\ -3& 0& 5\\ -3& -3& -3\end{array}\right]$

${M}_{2}=\left[\begin{array}{ccc}5& 5& 5\\ -3& 0& -3\\ -3& -3& -3\end{array}\right]$ ,

${M}_{4}=\left[\begin{array}{ccc}5& -3& -3\\ 5& 0& -3\\ 5& -3& -3\end{array}\right]$ , ${M}_{5}=\left[\begin{array}{ccc}-3& -3& -3\\ 5& 0& -3\\ 5& 5& -3\end{array}\right]$

${M}_{6}=\left[\begin{array}{ccc}-3& -3& -3\\ -3& 0& -3\\ 5& 5& 5\end{array}\right]$ , ${M}_{7}=\left[\begin{array}{ccc}-3& -3& -3\\ -3& 0& 5\\ -3& 5& 5\end{array}\right]$

$LD{P}_{k}={\sum }_{i=0}^{7}{b}_{i}\left({m}_{i}-{m}_{k}\right){2}^{i}$ (1)

${m}_{k}$ 是第k个最大边缘响应值。当k = 3时，相应的LDP算子的编码方式如图1所示。图2所示为掌

Figure 1. The code process of the LDP operator

Figure 2. The palmprint image based the LDP coding

2.2. 掌纹特征描述

3. PNN分类

1989年D.F. Specht 博士首先提出概率神经网络(PNN)，它是由径向基神经网络变化而来，是一种基于 Bayes 分类规则与 Parzen 窗的概率密度函数估计方法的一种并行算法 [19] 。它是由输入层、模式层、求和层和输出层四个结构层构成，其结构图如下图4所示。

${M}_{ij}\left(X\right)=\mathrm{exp}\left(-\frac{{\sum }_{i=1}^{n}\left({\left({X}_{i}-{w}_{ij}\right)}^{2}/\left({X}_{i}+{w}_{ij}\right)\right)}{\sigma }\right)$ (2)

Figure 3. The joint process of feature histograms

Figure 4. The structure diagram of Probabilistic Neural Networks

${S}_{i}\left(X\right)={\sum }_{i=1}^{{N}_{1}}{w}_{ij}{M}_{ij}\left(X\right),\text{\hspace{0.17em}}\text{\hspace{0.17em}}i=1,2,\cdots ,n$ (3)

4. 实验结果和分析

Table 1. The classification performance in different approaches (1)

Table 2. The classification performance in different approaches (2)

Table 3. The average running time(s) in different algorithms

5. 结论

Research of Palmprint Identification Algorithm Based on LDP and PNN[J]. 计算机科学与应用, 2018, 08(04): 464-471. https://doi.org/10.12677/CSA.2018.84051

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