Advertisement

Deep learning for finger-knuckle-print identification system based on PCANet and SVM classifier

  • Rachid ChlaouaEmail author
  • Abdallah Meraoumia
  • Kamal Eddine Aiadi
  • Maarouf Korichi
Original Paper

Abstract

Biometric technology knows a large attention in the recent years. In the biometric security systems, the personal identity recognition depends on their behavioral, biological or physical characteristics. Currently, a number of biometrics technologies are developed and one of the most popular biometric trait is finger-knuckle-print (FKP) due to the user-friendly and the low cost. This paper presents a new approach, where the deep learning is applied to create a multi-modal biometric system based on images of FKP modalities which extracted their features by principal component analysis Network (PCANet). In the proposed structure, PCA is employed to learn two-stage of filter banks followed by simple binary hashing and block histograms for clustering at feature vectors, which is adopt as input for classification by linear multiclass Support Vector Machine (SVM). To improve the recognition rates, a multimodal biometric system based on matching score level fusion scheme was generated. Using an available FKP database, we conducted a series of identification experiments and the obtained results show that the design of our identification system achieves an excellent recognition rate and having high anti-counterfeiting capability.

Keywords

Biometrics Identification Finger-knuckle-print (FKP) Feature extraction Deep learning PCANet Data fusion 

Notes

References

  1. Angelov P, Gu X (2017) MICE: multi-layer multi-model images classifier ensemble. In: 3rd IEEE international conference on cybernetics (CYBCONF 2017), Exeter, UK, pp 1–8.  https://doi.org/10.1109/CYBConf.2017.7985788
  2. Baochang Z (2010) Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans Image Process 19(2):533–544MathSciNetCrossRefzbMATHGoogle Scholar
  3. Belgacem N, Fournier R, Nait-Ali A, Bereksi-Reguig F (2015) A novel biometric authentication approach using ECG and EMG signals. J Med Eng Technol 39(4):226–238CrossRefGoogle Scholar
  4. Chan T, Jia K, Gao S, Lu J, Zeng Z, Ma Y (2015) PCANet: a simple deep learning baseline for image classification. IEEE Trans Image Process 24:5017MathSciNetCrossRefGoogle Scholar
  5. Chang Y, Li W, Yang Z (2017) Network intrusion detection based on random forest and support vector machine. In: IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC), vol 1, pp 635–638Google Scholar
  6. Deng L (2013) A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion. In: Proceedings of IEEE international conference of acoustic speech signal process (ICASSP), pp 6669–6673Google Scholar
  7. Donahue J, Jia Y, Vinyals O (2013) DeCAF: a deep convolutional activation feature for generic visual recognition. Comput Sci 50(1):815–830Google Scholar
  8. Esposito A, Marinaro M, Oricchio D, Scarpetta S (2000) Approximation of continuous and discontinuous mappings by a growing neural RBF-based algorithm. Neural Netw 12:651–665CrossRefGoogle Scholar
  9. Feng ZY, Jin LW, Tao DP, Huang SP (2015) DLANet: a manifold-learning-based discriminative feature learning network for scene classification. Neurocomputing 157:11–21CrossRefGoogle Scholar
  10. Fierrez-Aguilar J, Ortega-Garcia J, Gonzalez-Rodriguez J (2005) Target dependent score normalization techniques and their application to signature verification. IEEE Trans Syst Man Cybern C Appl Rev 35(3):418–425CrossRefGoogle Scholar
  11. Ghazi MM, Ekenel HK (2016) A comprehensive analysis of deep learning based representation for face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, Las Vegas, USA, pp 34–41Google Scholar
  12. Gu X, Angelov PP (2018) Self-organising fuzzy logic classifier. Inf Sci 447:36–51.  https://doi.org/10.1016/j.ins.2018.03.004 MathSciNetCrossRefGoogle Scholar
  13. Gu X, Angelov PP, Zhang C, Atkinson PM (2018) A massively parallel deep rule-based ensemble classifier for remote sensing scenes. IEEE Geosci Remote Sens Lett 15(3):345–349.  https://doi.org/10.1109/LGRS.2017.2787421 CrossRefGoogle Scholar
  14. Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554MathSciNetCrossRefzbMATHGoogle Scholar
  15. Karki MV, Selvi SS (2013) Multimodal biometrics at feature level fusion using texture features. Int J Biometr Bioinf 7(1):58–73Google Scholar
  16. Kasabov N (2007) Evolving connectionist systems: the knowledge engineering approach, 2nd edn. Springer, New YorkzbMATHGoogle Scholar
  17. Kaur G, Yadav AK, Chaudhary S (2014) An improved approach to multibiometrics security. Int J Comput Sci Commun 5(1):181–187Google Scholar
  18. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems 25. Curran Associates, Inc., New York, pp 1097–1105Google Scholar
  19. Kumar A, Ravikant Ch (2009) Personal authentication using finger knuckle surface. IEEE Trans Inf Forensics Secur 4(1):98–109CrossRefGoogle Scholar
  20. Marchi E, Vesperini F, Eyben F, Squartini S, Schuller B (2015) A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks. In: IEEE international conference on acoustics, speech, and signal processing (ICASSP’15), IEEE, Brisbane, Australia, p 5Google Scholar
  21. Meraoumia A, Korichi M, Bendjenna H, Chitroub S (2016) Multispectral palmprint identification method using rotation invariant variance measures. In: IEEE international conference on information technology for organizations development (IT4OD), Fez, Morocco, pp 1–6Google Scholar
  22. Meraoumia A, Laimeche L, Bendjenna H, Chitroub S (2016) Do we have to trust the deep learning methods for palmprints identification? In: Proceedings of the mediterranean conference on pattern recognition and artificial intelligence, Tebessa, Algeria, pp 85–91Google Scholar
  23. Nakanishi I, Sodani Y (2010) SVM-based biometric authentication using intra-body propagation signals. In: Seventh IEEE international conference on advanced video and signal based surveillance (AVSS), pp 561–566Google Scholar
  24. Oquab M, Bottou L, Laptev I, Sivic J (2014) Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of IEEE conference of computer vision pattern recognition, pp 1717–1724Google Scholar
  25. Pang S, Ban T, Kadobayashi Y, Kasabov N (2011) Personalized mode transductive spanning SVM classification tree. Inf Sci 181(11):2071–2085CrossRefGoogle Scholar
  26. Razavian AS, Azizpour H, Sullivan J (2014) CNN features off-the-shelf: an astounding baseline for recognition. In: Computer vision and pattern recognition workshops, pp 24–29Google Scholar
  27. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117CrossRefGoogle Scholar
  28. Sudhamani MJ, Venkatesha MK, Radhika KR (2012) Revisiting feature level and score level fusion techniques in multimodal biometrics system. In: Proceedings of international conference on multimedia computing and systems (ICMCS), pp 881–885Google Scholar
  29. Taigman Y, Yang M, Ranzato MA, Wolf L (2014) DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Columbus, USA, pp 23–28, 1701–1708Google Scholar
  30. The Hong Kong Polytechnic University (PolyU) (2018) Finger-Knuckle-Print Database. http://www.comp.polyu.edu.hk/~biometrics/FKP.htm
  31. Tian L, Fan CX, Ming Y, Jin Y (2015) Stacked PCA network (SPCANet): an effective deep learning for face recognition. In: Proceedings of IEEE international conference digital signal processing, pp 1039–1043Google Scholar
  32. Upadhayay R, Yadav RK (2013) Kernel principle component analysis in face recognition system: a survey. Int J Adv Res Comput Sci Softw Eng 3(6):348–353Google Scholar
  33. Zhang D, Kong W, You J, Wong M (2003) Online palmprint identification. IEEE Trans Pattern Anal Mach Intell 25(9):10411050Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Lab. de Génie Électrique, Fac. des Nouvelles Technologies de l’Information et de la CommunicationUniversity of OuarglaOuarglaAlgeria
  2. 2.LAboratory of Mathematics, Informatics and Systems (LAMIS)University of Larbi TebessiTebessaAlgeria

Personalised recommendations