Palmprint recognition based on convolutional neural network-Alexnet

• Autorzy: Gong Weiyong , Zhang Xinman , Deng Bohua , Xu Xuebin

Identyfikatory

DOI

10.15439/2019F248

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

In the classic algorithm, palmprint recognition requires extraction of palmprint features before classification and recognition, which will affect the recognition rate. To solve this problem, this paper uses the convolutional neural network (CNN) structure Alexnet to realize palmprint recognition. First, according to the characteristics of the geometric shape of palmprint, the ROI area of palmprint was cut out. Then the ROI area after processing is taken as input layer of convolutional neural network. Next the PRelu activation function is used to train the network to select the best learning rate and super parameters. Finally, the palmprint was classified and identified. The method was applied to PolyU Multi-Spectral Palmprint Image Database and PolyU 2D+3D Palmprint Database, and the recognition rate of a single spectrum was up to 99.99%.

Słowa kluczowe

EN

convolutional neural nets; feature extraction; image fusion; artificial intelligence; palmprint recognition

PL

konwolucyjna sieć neuronowa; ekstrakcja cech; fuzja obrazów; sztuczna inteligencja; rozpoznawanie odcisków palców

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 313–--316
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wykr.

Twórcy

autor

Gong Weiyong

• School of Electronics and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, No.28 xianning west road Xi’an, China

autor

Zhang Xinman

• School of Electronics and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, No.28 xianning west road Xi’an, China

autor

Deng Bohua

• School of Electronics and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, No.28 xianning west road Xi’an, China

autor

Xu Xuebin

• Guangdong Xi'an Jiaotong University Academy. No. 3, Daliangshuxiang East Road Foshan, China

Bibliografia

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• 3. Kong A, Zhang D, Kamel M, “A survey of palmprint recognition,” Pattern Recognition, 2009, vol.42, pp.1408-1418, July. 2009.http://dx.doi.org/10.1016/j.patcog.2009.01.018
• 4. Wu Y P, Tian J W, Xu D, et al. “Palmprint Recognition Based on RB K-means and Hierarchical SVM,” International Conference on Machine Learning & Cybernetics, 2007. http://dx.doi.org/10.1109/ICMLC.2007.4370778
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• 7. Jia W, Gui J, Hu R X,”Palmprint Recognition Using Kernel Spectral Regression Discriminant Analysis and HOG Representation,” International Workshop on Emerging Techniques & Challenges for Hand-based Biometrics, 2010. http://dx.doi.org/10.1109/ETCHB.2010.5559288
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• 13. He K, Zhang X, Ren S, “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification,”2015 IEEE International Conference on Computer Vision (ICCV). http://dx.doi.org/10.1109/ICCV.2015.123
• 14. W. Li, D. Zhang, L. Zhang, G. Lu, and J. Yan, " 3-D Palmprint Recognition with Joint Line and Orientation Features, "IEEE Transactions on Systems, Man, and Cybernetics, Part C, vol.41,pp.274-279, March 2011. http://dx.doi.org/10.1109/TSMCC.2010.2055849
• 15. David Zhang, Zhenhua Guo, Guangming Lu, etc., "An Online System of Multi-spectral Palmprint Verification", IEEE Transactions on Instrumentation and Measurement, vol. 59, pp. 480-490, Feb. 2010. http://dx.doi.org/10.1109/tim.2009.2028772

Uwagi

1. This work is supported by National Natural Science Foundation (No. 61673316), and Major Science and Technology Project of Guangdong Province (No. 2015B010104002).

2. Track 2: Computer Science & Systems

3. Technical Session: 12th International Symposium on Multimedia Applications and Processing

4. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).