Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (9): 2591-2600.doi: 10.13229/j.cnki.jdxbgxb.20220044

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Lightweight iris segmentation model based on multiscale feature and attention mechanism

Guang HUO1(),Da-wei LIN1,Yuan-ning LIU2,3(),Xiao-dong ZHU2,3,Meng YUAN2,Di GAI4   

  1. 1.College of Computer Science，Northeast Electric Power University，Jilin 132012，China
    2.College of Computer Science and Technology，Jilin University，Changchun 130012，China
    3.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education，Jilin University，Changchun 130012，China
    4.School of Software，Nanchang University，Nanchang 330047，China
  • Received:2022-01-09 Online:2023-09-01 Published:2023-10-09
  • Contact: Yuan-ning LIU E-mail:yanhuo1860@126.com;lyn@jlu.edu.com

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Abstract:

Aiming at the problem that deep learning-based iris segmentation models need a large number of parameters， computation cost， and space occupation， a lightweight iris segmentation model is proposed in this paper. First， the feature extraction network of Linknet is replaced with the improved lightweight deep neural network MobileNetv3. This design significantly improves the efficiency of the model while maintaining accuracy. Then， in order to reduce the loss of iris feature information， a multiscale feature extraction module is designed in this paper. Once again， an efficient parallel attention mechanism is introduced to suppress noise interference and enhance the weight of iris region pixels. Finally， the proposed model was compared with other iris segmentation models on three iris databases， and the results showed that the model achieved a better balance between iris segmentation accuracy and efficiency.

Key words: computer application, iris segmentation, deep learning, lightweight network, attention mechanism, multiscale feature

CLC Number: 

  • TP391.41

Fig.1

Overall network structure"

Fig.2

Design of feature extraction module"

Fig.3

Multiscale feature extraction module"

Fig.4

SA module"

Fig.5

Iris images"

Table 1

Comparison with conventional algorithms"

数据集方法MIOUF1RER
CASIA-V4Caht［17］0.80700.7651-0.1470
Ifpp［18］0.78800.6378-0.2372
Wahet［19］0.80900.8949-0.0842
Osiris［20］-0.89850.97320.0673
IFPP［21］-0.86860.91740.2372
本文0.97390.98670.97620.0147
IITDAhmad［22］-0.9520--
GST［23］-0.33930.4259-
本文0.96990.98470.97510.0163
UBIRIS.V2Caht［17］-0.1048-0.4809
Ifpp［18］-0.2899-0.3970
Wahet［19］-0.1977-0.4498
Osiris［20］-0.18650.2646-
IFPP［21］-0.28520.4438-
本文0.95400.97600.95890.0224

Table 2

Comparison with algorithms based on deep learning"

数据集方法MIOUF1RER
CASIA-V4FCEDNs-original［24］-0.8821-0.0588
FCEDNs-basic［24］-0.9072-0.0438
FCEDNs-bayesian-basic［24］-0.9192-0.0407
RTV-L［25］0.78110.87550.8095-
DeepLabV3［25］0.88210.93210.9013-
UNet［26］0.95060.9723--
FD-UNet［27］-0.9736-0.0125
DFCN［10］-0.98280.98290.0118
Linknet［9］0.96730.98330.96560.0191
MFFIris-UNet［25］0.94610.9714--
本文0.97390.98670.97620.0147
IITDFCEDNs-original［24］-0.8661-0.0588
FCEDNs-basic［24］-0.9072-0.0438
FCEDNs-bayesian-basic［24］-0.8489-0.0701
FD-UNet［27］-0.9481-0.0258
DFCN［10］-0.98120.98060.0137
Linknet［9］0.96410.98170.97920.0173
本文0.96990.98470.97750.0163
UBIRIS.V2DeepLabV3［25］0.70240.87550.8517-
UNet［26］0.93620.9553--
Linknet［9］0.95250.97250.95250.0254
Wang［28］0.9535---
MFFIris-UNet［25］0.94280.96590.9287-
本文0.95400.97600.95890.0224

Table 3

Comparison of the number of parameters， computation amount and storage space of different methods"

方法参数量/M计算量/GMac存储空间/GB
DeepLabV3［25］18.86--
U-Net［26］34.5365.510.517
DFCN［10］142.50--
Wang［28］6.21--
Linknet［9］9.820.8220.035
本文0.250.4140.035

Fig.6

Segmentation results on the CASIA-V4 database"

Fig.7

Segmentation results on the IITD database"

Fig.8

Segmentation results on the UBIRIS.V2 database"

Table 4

Results of model ablation experiments"

数据集方法MIOUF1RER
CASIA-V4基准网络0.96890.98380.97020.0183
基准网络+多尺度特征提取模块0.97220.98590.97430.0158
基准网络+SA模块0.97290.98620.97220.0162
本文算法0.97390.98670.97620.0147
IITD基准网络0.96240.98080.96620.0212
基准网络+多尺度特征提取模块0.96800.98370.97790.0163
基准网络+SA模块0.96660.98300.97270.0181
本文算法0.96990.98470.97750.0163
UBIRIS.V2基准网络0.94590.97110.95310.0238
基准网络+多尺度特征提取模块0.95060.97360.95420.0236
基准网络+SA模块0.95130.97450.95790.0232
本文算法0.95400.97600.95890.0224

Fig.9

Visualization results of different networks"

Fig.10

Segmentation results of different networks"

Fig.11

Segmentation result"

Table 5

Experiment results"

方法准确率/%
U-Net［26］96.73
Linknet［9］97.86
本文98.92
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