An Approach for Privacy-Enhancing Actions Using Cryptography for Facial Recognition on Database

  • Arpankumar G. Raval
  • Harshad B. BhadkaEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 52)


In the era of digital security, face recognition is highly demanded with other biometric strategies like fingerprints, iris recognition, and hand geometry for identification. In these existing ways, there are some disadvantages like time taken, pose, illumination problem, and also age effects. With this, it has been noticed that the stored images are not secured, and it is possible of cheating with the restriction in non-tolerable areas of security. Keeping these aspects in mind, this work has proposed mixture of two popular methods with cipher images to be saved in the database. Proposed method also focusing on fastest key generation for encryption and can deal with more subjects and less false match rate. Both principal component analysis and local binary pattern algorithms are utilized, and encryption system has been included for storing the images and for recognition in this proposed method. Reducing the measurement of image is the functionality of the PC. An algorithm and LBP describe the binary pattern for neighbor pixels and generate pattern for the mapping. So proposed work will increase the true matching rate and decrease the false match rate with cryptography. This proposed method is appropriate for real-time application.


Principal component analysis Local binary pattern Security Cryptography Encryption Decryption PCA LBP FMR RR Eigenvalue Eigenvector 


  1. 1.
    Solanki K (2017) A novel approach to prevent unauthorized access and to enhance the security of database templateGoogle Scholar
  2. 2.
    Image Encryption Algorithm. Mathsworks.ComGoogle Scholar
  3. 3.
    Jain AK, Klare B, Park U (2011) Face recognition: some challenges in forensics. In: Face and gesture, pp 726–733. IEEE Google Scholar
  4. 4.
    Upmanyu M, Namboodiri AM, Srinathan K, Jawahar CV (2010) Blind authentication: a secure crypto-biometric verification protocol. IEEE Trans Inf Forensics Secur 5(2):255–268CrossRefGoogle Scholar
  5. 5.
    Simoens K, Bringer J, Chabanne H, Seys S (2012) A framework for analyzing template security and privacy in biometric authentication systems. IEEE Trans Inf Forensics Secur 7(2):833–841CrossRefGoogle Scholar
  6. 6.
    Patil B, Yardi A (2013) Time face recognition by varing number of eigenvalues. Int J Adv Sci Tech Res 3(1). ISSN 2249-9954Google Scholar
  7. 7.
    Nagar A, Nandakumar K, Jain AK (2011) Multibiometric cryptosystems based on feature-level fusion. IEEE Trans Inf Forensics Secur 7(1):255–268CrossRefGoogle Scholar
  8. 8.
    Kekre HB, Thepade SD, Maloo A (2010) Eigenvectors of covariance matrix using row mean and column mean sequences for face recognition. Int J Biometrics Bioinform (IJBB) 4(2):42–50Google Scholar
  9. 9.
    Zou WW, Yuen PC (2011) Very low resolution face recognition problem. IEEE Trans Image Process 21(1):327–340MathSciNetCrossRefGoogle Scholar
  10. 10.
  11. 11.
    Ren J, Jiang X, Yuan J (2013) A complete and fully automated face verification system on mobile devices. Pattern Recogn 46(1):45–56CrossRefGoogle Scholar
  12. 12.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  13. 13.
    Solanki K (2017) Elimination of angular problem in face recognition. Int J Eng Res Technol (IJERT) 06(12)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.C. U. Shah UniversityWadhwanIndia
  2. 2.FITCS Parul UniversityVadodaraIndia

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