A Novel Finger-Knuckle-Print Recognition Based on Batch-Normalized CNN

  • Yikui Zhai
  • He Cao
  • Lu CaoEmail author
  • Hui Ma
  • Junyin Gan
  • Junying Zeng
  • Vincenzo Piuri
  • Fabio Scotti
  • Wenbo Deng
  • Yihang Zhi
  • Jinxin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)


Traditional feature extraction methods, such as Gabor filter and competitive coding, have been widely used in finger-knuckle-print (FKP) recognition. However, these methods focus on manually designed features which may not achieve satisfying results on FKP images. In order to solve this problem, a novel batch-normalized Convolutional Neural Network (CNN) architecture with data augmentation for FKP recognition is proposed. Firstly, a novel batch-normalized CNN is designed specifically for FKP recognition. Then, random histogram equalization is adopted as data augmentation here for training the CNN in FKP recognition. Meanwhile, batch-normalization is adopted to avoid overfitting during network training. Extensive experiments performed on the PolyU FKP database show that compared with traditional feature extraction method, the proposed method can not only extract more discriminative features, but also improve the accuracy of FKP recognition.


Finger-knuckle-print Batch-normalized Data augmentation 



This work is supported by National of Nature Science Foundation Grant (No. 61372193, No. 61771347), Guangdong Higher Education Outstanding Young Teachers Training Program Grant (No. SYQ2014001), Characteristic Innovation Project of Guangdong Province (No. 2015KTSCX 143, 2015KTSCX145, 2015KTSCX148), Youth Innovation Talent Project of Guangdong Province (No. 2015KQNCX172, No. 2016KQNCX171), Science and Technology Project of Jiangmen City (No. 201501003001556, No. 201601003002191), and China National Oversea Study Scholarship Fund.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yikui Zhai
    • 1
  • He Cao
    • 1
  • Lu Cao
    • 1
    Email author
  • Hui Ma
    • 1
  • Junyin Gan
    • 1
  • Junying Zeng
    • 1
  • Vincenzo Piuri
    • 3
  • Fabio Scotti
    • 3
  • Wenbo Deng
    • 1
  • Yihang Zhi
    • 2
  • Jinxin Wang
    • 1
  1. 1.School of Information EngineeringWuyi UniversityJiangmenChina
  2. 2.School of ComputerWuyi UniversityJiangmenChina
  3. 3.Dipartimento Di InformaticaUniversita’ degli Studi di MilanoMilanItaly

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