Skip to main content
SpringerLink
Log in

Infrared Handprint Classification Using Deep Convolution Neural Network

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Infrared handprint image is an image that applies infrared imaging technology to criminal investigation and other special scenes. It can be used to detect traces that cannot be directly observed under visible light.Efficient identification and analysis of handprint are conducive to obtaining more information for solving cases. However, due to thermal diffusion, the depth fuzzy feature of infrared handprint is not conducive to detection and classification, and the convolution neural network technology is widely used in the field of natural image classification because of its excellent feature extraction ability.Therefore, aiming at the problem of fuzzy infrared handprint classification, we design a novel convolution neural network, which includes a convolutional layer, small MBConv block and fully connected layer.We choose EfficientNet which is suitable for infrared handprint classification from classical convolution neural network as our basic network. And propose a small MBConv block to improve the network model, so that the network has fewer training parameters, effectively reduces the problem of over fitting, and improves the classification performance compared with the original model.We use our model for the automatic classification of infrared handprint images. The results show that our model achieves the average accuracyto 95.78% for multi-class classification, which is 2.19% higher than the original model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Li H, Qi X, Xie W (2020) Fast infrared and visible image fusion with structural decomposition[J]. Knowl Based Syst 204:106182

    Article  Google Scholar 

  2. Dunderdale C, Brettenny W, Clohessy C et al (2020) Photovoltaic defect classification through thermal infrared imaging using a machine learning approach[J]. Prog Photovolt Res Appl 28(3):177–183

    Article  Google Scholar 

  3. Fu D, Sun J, Yang T et al (2018) Target extraction of hand infrared trace images based on artificial targeting immunotherapy [J]. J Electron Inf Technol 40(002):346–352

    Google Scholar 

  4. Yang T, Fu D (2016) Extraction of blurred infrared targets based on a manifold regularized multiple-kernel model [J]. Chin J Eng 38(6):876–885

    Google Scholar 

  5. Yu X, Fu D (2014) Target extraction from blurred trace infrared images with a superstring galaxy template algorithm[J]. Infrared Phys Technol 64:9–12

    Article  Google Scholar 

  6. Yang D, Lu A, Ren D et al (2017) Rapid determination of biogenic amines in cooked beef using hyperspectral imaging with sparse representation algorithm[J]. Infrared Phys Technol 86:23–34

    Article  Google Scholar 

  7. Fabelo H, Ortega S, Casselden E et al (2018) SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples[J]. Sensors 18(12):4487

    Article  Google Scholar 

  8. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition[J]. Comput Ence

  9. Szegedy C, Ioffe S, Vanhoucke V et al (2016) Inception-v4, Inception-ResNet and the impact of residual connections on learning[J]

  10. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L (2018) MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pp 4510–4520

  11. Andrew H, Mark S, Grace C, Liang-Chieh C, Bo C, Mingxing T, Weijun W, Yukun Z, Ruoming P, Vijay V et al (2019) Searching for mobilenetv3. In: ICCV

  12. Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. CVPR

  13. Yu J, Tao D, Wang M et al (2015) Learning to rank using user clicks and visual features for image retrieval[J]. IEEE Trans Cybern 45(4):767–779

    Article  Google Scholar 

  14. Zhang J, Yu J, Tao D (2018) Local deep-feature alignment for unsupervised dimension reduction[J]. IEEE Trans Image Process 27(5):2420–2432

    Article  MathSciNet  Google Scholar 

  15. Chen X, Xiang S, Liu CL et al (2014) Vehicle detection in satellite images by hybrid deep convolutional neural networks[C]. In: Pattern recognition, IEEE, pp 1797–1801

  16. Fairuz S, Habaebi MH, Elsheikh EMA et al (2018) Convolutional neural network-based finger vein recognition using near infrared Images[C]. In: 2018 7th international conference on computer and communication engineering (ICCCE)

  17. Song W, Li S, Fang L et al (2018) Hyperspectral Image Classification With Deep Feature Fusion Network[J]. IEEE Trans Geosci Remote Sens 56(6):3173–3184

    Article  Google Scholar 

  18. Kanavati F, Toyokawa G, Momosaki S et al (2020) Weakly-supervised learning for lung carcinoma classification using deep learning[J]. Entific Rep 10(1):1–11

    Google Scholar 

  19. Liu Q, Li Z, Shuai S et al (2020) Spectral group attention networks for hyperspectral image classification with spectral separability analysis[J]. Infrared Phys Technol 5:103340

    Article  Google Scholar 

  20. Hong C, Yu J, Zhang J et al (2019) Multimodal face-pose estimation with multitask manifold deep learning[J]. IEEE Trans Industr Inf 15(7):3952–3961

    Article  Google Scholar 

  21. Yu J, Tan M, Zhang H et al (2019) Hierarchical Deep Click Feature Prediction for Fine-grained Image Recognition[J]. IEEE Trans Pattern Anal Mach Intell 99:1–10

    Google Scholar 

  22. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA, pp 770–778

  23. Hu J, Shen L, Albanie S et al (2017) Squeeze-and-Excitation Networks[J]. IEEE Trans Pattern Anal Mach Intell 99:7132–7141

    Google Scholar 

  24. Tan M, Le QV (2019) EfficientNet: rethinking model scaling for convolutional neural networks[J]

  25. Tan M, Chen B, Pang R et al (2018) MnasNet: platform-aware neural architecture search for mobile[J]

  26. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML

  27. Jiang LY (2019) Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNetmodule[J]. PLoS One

  28. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: CVPR, pp 1251–1258

  29. Ma N, Zhang X, Zheng H-T, Sun J (2018) Shufflenet v2: practical guidelines for efficientcnn architecture design. In: ECCV

  30. Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) GhostNet: more features from cheap operations. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), Seattle, WA, USA, pp 1577–1586

  31. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034

  32. Szegedy C, Vanhoucke V, Ioffe S et al (2016) Rethinking the Inception Architecture for Computer Vision[C]. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2818–2826

Download references

Acknowledgements

This research is supported by the National Natural Science Foundation of China (No. 61502340). The Natural Science Foundation of Tianjin (No. 18JCQNJC01000).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China

    Zijie Zhou

  2. School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin, China

    Baofeng Zhang & Xiao Yu

Authors
  1. Zijie Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baofeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiao Yu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Z., Zhang, B. & Yu, X. Infrared Handprint Classification Using Deep Convolution Neural Network. Neural Process Lett 53, 1065–1079 (2021). https://doi.org/10.1007/s11063-021-10429-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-021-10429-6

Keywords

Search

Navigation