卷积神经网络在掌纹识别中的性能评估

王海纶; 李书杰; 贾伟; 刘晓平

doi:10.11834/jig.180605

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专辑

卷积神经网络在掌纹识别中的性能评估
Performance evaluation of convolutional neural network in palmprint recognition
2019年24卷第8期页码：1231-1248
收稿：2018-10-29，

修回：2019-1-21，

纸质出版：2019-08-16
DOI： 10.11834/jig.180605
稿件说明：

移动端阅览

王海纶, 李书杰, 贾伟, 刘晓平. 卷积神经网络在掌纹识别中的性能评估[J]. 中国图象图形学报, 2019,24(8):1231-1248. DOI： 10.11834/jig.180605.

Hailun Wang, Shujie Li, Wei Jia, Xiaoping Liu. Performance evaluation of convolutional neural network in palmprint recognition[J]. Journal of Image and Graphics, 2019, 24(8): 1231-1248. DOI： 10.11834/jig.180605.

摘要

目的

掌纹识别技术作为一种新兴的生物特征识别技术越来越受到广泛重视。深度学习是近10年来人工智能领域取得的重要突破。但是，基于深度学习的掌纹识别相关研究还比较初步，尤其缺乏深入的分析和讨论，且已有的工作使用的都是比较简单的神经网络模型。为此，本文使用多种卷积神经网络对掌纹识别进行性能评估。

方法

选取比较典型的8种卷积神经网络模型，在5个掌纹数据库上针对不同网络模型、学习率、网络层数、训练数据量等进行性能评估，展开实验，并与经典的传统掌纹识别方法进行比较。

结果

在不同卷积神经网络识别性能评估方面，ResNet和DenseNet超越了其他网络，并在PolyU M_B库上实现了100%的识别率。针对不同学习率、网络层数、训练数据量的实验发现，5×10

-5

为比较合适的识别率；网络层数并非越深越好，VGG-16与VGG-19的识别率相当，ResNet层数由18层逐渐增加到50层，识别率则逐渐降低；参与网络训练的数据量总体来说越多越好。对比传统的非深度学习方法，卷积神经网络在识别效果方面还存在一定差距。

结论

实验结果表明，对于掌纹识别，卷积神经网络也能获得较好的识别效果，但由于训练数据量不充分等原因，与传统算法的识别性能还有差距。基于卷积神经网络的掌纹识别研究还需要进一步深入开展。

Abstract

Objective

In recent years

as an emerging biometrics technology

low-resolution palmprint recognition has attracted attention due to its potential for civilian applications. Many effective palmprint recognition methods have been proposed. These traditional methods can be roughly divided into categories

such as texture-based

line-based

subspace learning-based

correlation filter-based

local descriptor-based

and orientation coding-based. In the past decade

deep learning was the most important technique in the field of artificial intelligence

introducing performance breakthroughs in many fields such as speech recognition

natural language processing

computer vision

image and video analysis

and multimedia. In the field of biometrics

especially in face recognition

deep learning has become the most mainstream technology. However

research on deep learning-based palmprint recognition remains at the preliminary stage. Research on deep learning-based palmprint recognition is relatively rare

and in-depth analysis and discussion on deep learning-based palmprint recognition is scarce. In addition

most existing work on deep learning-based palmprint recognition exploited simple networks only. In palmprint databases

the palmprint images were usually captured in two different sessions. In traditional palmprint recognition work

the images captured in the first session were usually treated as the training data

and the images captured in the second session were typically used as the test data. However

in existing work on deep learning-based palmprint recognition

the images captured in the first and second sessions are exploited as the training data

which leads to a high recognition accuracy. In this study

we evaluate the performance of various convolutional neural networks (CNNs) in palmprint recognition to thoroughly investigate the problem of deep learning-based palmprint recognition.

Method

We systematically review the classic CNNs in recent years and analyze the structure of various networks and their underlying connections. Then

we perform a large-scale performance evaluation for palmprint recognition. First

we select eight typical CNN networks

namely

AlexNet

VGG

Inception_v3

ResNet

Inception_v4

Inception_ResNet_v2

DenseNet

and Xception

and evaluate these networks on five palmprint databases to determine the best network. We choose the pretrained model in ImageNet Large Scale Visual Recognition Challenge for training because training the CNN model in the case of insufficient data (the scale of the dataset is small) is time consuming and may lead to poor results. Second

we conduct evaluations by using six learning rates from large to small to analyze the impact on performance and obtain the suitable learning rate. Third

we compare the performance of VGG-16 and VGG-19 and ResNet18

ResNet34

and ResNet50 in the evaluation on different layer numbers of the network. Fourth

starting from a single training data

we gradually increase the data amount until the training data contains all the data of the first session to analyze the influence of different training data quantities on performance. Finally

the performance of CNNs is compared with that of several traditional methods

such as competitive code

ordinal code

RLOC

and LLDP.

Result

Experimental results on eight CNNs with different structures show that ResNet18 outperforms other networks and can achieve 100% recognition rate on the PolyU M_B database. The performance of DenseNet121 is similar to that of ResNe18

and the performance of AlexNet is poor. To evaluate the learning rate

results show that 5×10

-5

is suitable for the palmprint dataset used in this study. If the learning rate is too large

then the performance of these CNNs will be poor. In addition

the appropriate learning rate of the VGG network is 10

-5

. The performance evaluation of different numbers of network layers indicated that the recognition rate of VGG-16 and VGG-19 is similar. As the layer number of ResNet increases from 18 to 34 and to 50

the recognition rate gradually decreases. Generally speaking

more data involved in network training results in improved performance. In the early stage of the increase in the amount of data

the performance is significantly improved. A comparison of the performance of CNNs with that of traditional non-deep learning methods shows that the performance of CNNs is equivalent to that of non-deep learning methods on the PolyU M_B database. On other databases

the performance of CNNs is worse than that of traditional non-deep learning methods.

Conclusion

This paper reviews the CNNs proposed in the literature and conducts a large-scale performance evaluation of palmprint recognition on five different palmprint databases under different network structures

learning rates

network layers

and training data amounts. Results show that ResNet is suitable for palmprint recognition and that 5×10

-5

is an appropriate learning rate

which can help researchers engaged in deep learning and palmprint recognition. We also compared the performance of CNNs with that of four traditional methods. The overall performance of CNN is slightly worse than that of traditional methods

but we can still see the great potential of deep learning methods.

关键词

Keywords

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