Advertisement

Finger kunckcle patterns based person recognition via bank of multi-scale binarized statistical texture features

  • Abdelouahab AttiaEmail author
  • Mourad Chaa
  • Zahid Akhtar
  • Youssef Chahir
Original Paper
  • 25 Downloads

Abstract

This paper proposes a novel finger knuckle patterns (FKP) based biometric recognition system that utilizes multi-scale bank of binarized statistical image features (B-BSIF) due to their improved expressive power. The proposed system learns a set of convolution filters to form different BSIF feature representations. Later, the learnt filters are applied on each FKP traits to determine the top performing BSIF features and respective filters are used to create a bank of features named B-BSIF. In particular, the presented framework, in the first step, extracts the region of interest (ROI) from FKP images. In the second step, the B-BSIF coding method is applied on ROIs to obtain enhanced multi-scale BSIF features characterized by top performing convolution filters. The extracted feature histograms are concatenated in the third step to produce a large feature vector. Then, a dimensionality reduction procedure, based on principal component analysis and linear discriminant analysis techniques (PCA + LDA), is carried out to attain compact feature representation. Finally, nearest neighbor classifier based on the cosine Mahalanobis distance is used to ascertain the identity of the person. Experiments with the publicly available PolyU FKP dataset show that the presented framework outperforms previously-proposed methods and is also able to attain very high accuracy both in identification and verification modes.

Keywords

Biometric Image local descriptor BSIF Dimensionality reduction Classification 

Notes

References

  1. Adeoye OS (2010) A survey of emerging biometric technologies. Int J Comput Appl 9(10):1–5Google Scholar
  2. Akhtar Z, Alfarid N (2011) Secure learning algorithm for multimodal biometric systems against spoof attacks. In: Proc. international conference on information and network technology (IPCSIT), vol. 4, pp 52–57Google Scholar
  3. Akhtar Z, Fumera G, Marcialis GL, Roli F (2011) Robustness evaluation of biometric systems under spoof attacks. In: International conference on image analysis and processing, pp 159–168Google Scholar
  4. Angelov PP, Gu X (2018) Deep rule-based classifier with human-level performance and characteristics. Inf Sci (Ny) 463–464:196–213CrossRefGoogle Scholar
  5. Bao R-J, Rong H-J, Angelov PP, Chen B, Wong PK (2018) Correntropy-based evolving fuzzy neural system. IEEE Trans Fuzzy Syst 26(3):1324–1338CrossRefGoogle Scholar
  6. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRefGoogle Scholar
  7. Chaa M, Boukezzoula N, Meraoumia A (2018) Features-level fusion of reflectance and illumination images in finger-knuckle-print identification system. Int J Artif Intell Tools 27(3):1850007CrossRefGoogle Scholar
  8. Chlaoua R, Meraoumia A, Aiadi KE, Korichi M (2018) Deep learning for finger-knuckle-print identification system based on PCANet and SVM classifier. Evol Syst.  https://doi.org/10.1007/s12530-018-9227-y CrossRefGoogle Scholar
  9. El-Tarhouni W, Shaikh MK, Boubchir L, Bouridane A (2014) Multi-scale shift local binary pattern based-descriptor for finger-knuckle-print recognition. In: 26th International Conference on Microelectronics (ICM), 2014, pp 184–187Google Scholar
  10. Jain AK, Flynn P, Ross AA (2007) Handbook of biometrics. Springer, BerlinGoogle Scholar
  11. Kannala J, Rahtu E (2012) Bsif: binarized statistical image features. In: 21st International Conference on Pattern Recognition (ICPR), 2012, pp 1363–1366Google Scholar
  12. Kong T, Yang G, Yang L (2014) A hierarchical classification method for finger knuckle print recognition. EURASIP J Adv Signal Process 2014(1):44MathSciNetCrossRefGoogle Scholar
  13. Morales A, Travieso CM, Ferrer MA, Alonso JB (2011) Improved finger-knuckle-print authentication based on orientation enhancement. Electron Lett 47(6):380–381CrossRefGoogle Scholar
  14. Nigam A, Tiwari K, Gupta P (2016) Multiple texture information fusion for finger-knuckle-print authentication system. Neurocomputing 188:190–205CrossRefGoogle Scholar
  15. PolyU (2010) The Hong Kong polytechnic university (PolyU) Finger-Knuckle-Print Database [Online]. http://www.comp.polyu.edu.hk/ biometrics/FKP.html
  16. Rani E, Shanmugalakshmi R (2013) Finger knuckle print recognition techniques—a survey. Int J Eng Sci 2(11):62–69Google Scholar
  17. Shariatmadar ZS, Faez K (2013) Finger-knuckle-print recognition via encoding local-binary-pattern. J Circuits Syst Comput 22(6):1350050CrossRefGoogle Scholar
  18. Shariatmadar ZS, Faez K (2014) Finger-Knuckle-Print recognition performance improvement via multi-instance fusion at the score level. Opt J Light Electron Opt 125(3):908–910CrossRefGoogle Scholar
  19. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRefGoogle Scholar
  20. Zeinali B, Ayatollahi A, Kakooei M (2014) “A novel method of applying directional filter bank (DFB) for finger-knuckle-print (FKP) recognition. In: 22nd Iranian Conference on Electrical engineering (ICEE), 2014, pp 500–504Google Scholar
  21. Zhai Y et al. (2018) A novel finger-knuckle-print recognition based on batch-normalized CNN. In: Chinese conference on biometric recognition, pp 11–21Google Scholar
  22. Zhang L, Li H (2012) Encoding local image patterns using Riesz transforms: With applications to palmprint and finger-knuckle-print recognition. Image Vis Comput 30(12):1043–1051MathSciNetCrossRefGoogle Scholar
  23. Zhang L, Zhang L, Zhang D, Zhu H (2010) Online finger-knuckle-print verification for personal authentication. Pattern Recognit 43(7):2560–2571CrossRefGoogle Scholar
  24. Zhang L, Zhang L, Zhang D, Zhu H (2011) Ensemble of local and global information for finger–knuckle-print recognition. Pattern Recognit 44(9):1990–1998CrossRefGoogle Scholar
  25. Zhang D, Lu G, Zhang L (2018a) Finger-knuckle-print verification with score level adaptive binary fusion. In: Advanced Biometrics. Springer, Cham, pp 151–174CrossRefGoogle Scholar
  26. Zhang D, Lu G, Zhang L (2018b) Finger-knuckle-print verification. In: Advanced biometrics. Springer, Cham, pp 85–109CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Abdelouahab Attia
    • 1
    Email author
  • Mourad Chaa
    • 2
  • Zahid Akhtar
    • 3
  • Youssef Chahir
    • 4
  1. 1.Computer Science Department, Faculty of Mathematics and InformaticsUniversity of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria
  2. 2.Lab. ELEC, Faculty of New Technology of Information and CommunicationOuargla UniversityOuarglaAlgeria
  3. 3.University of MemphisMemphisUSA
  4. 4.Lab. Image Team GREYC-CNRS UMR 6072 University of CaenCaenFrance

Personalised recommendations