Skip to main content
SpringerLink
Log in

Face authentication in encrypted domain based on correlation filters

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Privacy and security are main concerns in face authentication by cloud or semi-honest server. To deal with this problem, a face authentication method based on correlation filters in encrypted domain is presented in this paper. This method includes enrollment and authentication steps using homomorphic cryptosystem where public key and correlation filter are shared between client and server. Such system obviates the need for trusting the server and raises a privacy issue by performing all computation in the encrypted domain without needing any decryption, even during face authentication process. In the proposed method, correlation operation, peak to sidelobe ratio (PSR) measurement, and threshold comparison are performed by server in the encrypted domain. We show that the proposed method is secure against typical encryption attacks. Experimental results on LFW, Yale-B, and FERET face databases demonstrate that, the recognition rate in the encrypted domain is almost the same as plain domain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Alfalou A, Brosseau C, Kaddaha W (2015) Optimization of decision making for face recognition based on nonlinear correlation plane. Opt Commun 343:22–27. https://doi.org/10.1016/j.optcom.2015.01.017

    Article  Google Scholar 

  2. Atawneh S, Almomani A, Al-Bazar H, Sumari P, Gupta B (2017) Secure and imperceptible digital image steganographic algorithm based on diamond encoding in dwt domain. Multimed Tools Appl 76(18):18451–18472. https://doi.org/10.1007/s11042-016-3930-0

    Article  Google Scholar 

  3. Bai Y, Zhuo L, Cheng B, Peng Y (2014) Surf feature extraction in encrypted domain. IEEE International conference on Multimed Expo (ICME). https://doi.org/10.1109/ICME.2014.6890170

  4. Banerjee P, Datta A (2013) Generalized regression neural network trained pre processing of frequency domain correlation filter for improved face recognition and its optical implementation. Opt Laser Technol 45:217–227. https://doi.org/10.1016/j.optlastec.2012.07.001

    Article  Google Scholar 

  5. Barni M, Bianchi T, Catalano D, Raimondo MD, Labati RD, Failla P, Fiore D, Lazzeeretti R, Piuri V, Piva A, Scotti F (2010) A privacy- compliant fingerprint recognition system based on homomorphic encryption and fingercode templates. In: Fourth IEEE international conference on biometrics: theory applications and systems (BTAS), pp 1–7. https://doi.org/10.1109/BTAS.2010.5634527

  6. Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720. https://doi.org/10.1109/34.598228

    Article  Google Scholar 

  7. Boddeti VN, Kumar BVKV, Mahalanobis A (2013) Maximum margin correlation filter: a new approach for localization and classification. IEEE Trans Image Process 22:631–643. https://doi.org/10.1109/TIP.2012.2220151

    Article  MathSciNet  MATH  Google Scholar 

  8. Bolme DS, Draper BA, Beveridge JR (2009) Average of synthetic exact filters.. In: IEEE conference on computer vision and pattern recognition 2009 CVPR, pp 2105–2112. https://doi.org/10.1109/CVPR.2009.5206701

  9. Bolme DS, Lui YM, Draper BA, Beveridge JR (2010) Simple real-time human detection using a single correlation filter.. In: Twelfth IEEE international workshop on performance evaluation of tracking and surveillance (PETS-Winter), pp 1–8. https://doi.org/10.1109/PETS-WINTER.2009.5399555

  10. Canuto A, Pintro F, Xavier JJ (2013) Investigation fusion approaches in multi-biometric cancellable recognition. Expert Syst Appl 40(6):1971–1980. https://doi.org/10.1016/j.eswa.2012.10.002

    Article  Google Scholar 

  11. Daugman J (2002) How iris recognition works.. In: Proceedings of the 2002 international conference on image processing, 2002, vol 1, pp 33–36

  12. Erkin Z (2007) Protection and retrieval of encrypted multimedia content: when cryptography meets signal processing. EURASIP J Inf Secur 7:1–20. https://doi.org/10.1155/2007/78943

    Google Scholar 

  13. Erkin Z, Franz M, Guajardo J, Katzenbeisser S, Lagendijk I, Toft T (2009) Privacy preserving face recognition. Privac Preserv Technol 5672:235–253. https://doi.org/10.1007/978-3-642-03168-7_14

    Google Scholar 

  14. Gaxiolaa LN, Diaz-Ramireza VH, Tapiaa JJ, García-Martínez P (2016) Target tracking with dynamically adaptive correlation. Opt Commun 365:140–149. https://doi.org/10.1016/j.optcom.2015.11.077

    Article  Google Scholar 

  15. Gomez-Barrero M, Maiorana E, Galbally J, Campisi P, Fierrez J (2017) Multi-biometric template protection based on homomorphic encryption. Pattern Recogn 67:149–163. https://doi.org/10.1016/j.patcog.2017.01.024

    Article  Google Scholar 

  16. Gupta B, Agrawal D, Yamaguchi S (2016) Handbook of research on modern cryptographic solutions for computer and cyber security. IGI Global Publisher, USA. https://doi.org/10.4018/978-1-5225-0105-3

  17. Huang GB, Mattar M, Berg T, Learned-Miller E (2008) Labeled faces in the wild: a database for studying face recognition in unconstrained environments.. In: Workshop on faces in ‘real-life’ images: detection, alignment, and recognition, pp 07–49

  18. Iso/iec jtc1 sc27 security techniques (2011) ISO/IEC 24745:2011 Information Technology - Security Techniques - Biometric Information Protection

  19. Kaur H, Khanna P (2016) Biometric template protection using cancelable biometrics and visual cryptography techniques. Multimed Tools Appl 75 (23):16,333–16,361

    Article  Google Scholar 

  20. Kaur H, Khanna P (2017) Cancelable features using log-gabor filters for biometric authentication. Multimed Tools Appl 76(4):4673–4694

    Article  Google Scholar 

  21. Kumar S, Kumar-Singh S, Kumar-Singh A, Tiwari S, Singh RS (2017) Privacy preserving security using biometrics in cloud computing. Multimed Tools Appl 1–23. https://doi.org/10.1007/s11042-017-4966-5

  22. Lagendijk R, Erkin Z, Barni M (2013) Encrypted signal processing for privacy protection: conveying the utility of homomorphic encryption and multiparty computation. IEEE Signal Process Mag 30(1):82–105. https://doi.org/10.1109/MSP.2012.2219653

    Article  Google Scholar 

  23. Lee KC, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698. https://doi.org/10.1109/TPAMI.2005.92

    Article  Google Scholar 

  24. Li J, Yu C, Gupta B, Ren X (2017) Color image watermarking scheme based on quaternion hadamard transform and schur decomposition. Multimed Tools Appl 1–17. https://doi.org/10.1007/s11042-017-4452-0

  25. Lin GS, Chang MK, Chang YJ, Yeh CH (2016) A gender classification scheme based on multi-region feature extraction and information fusion for unconstrained images. Multimed Tools Appl 75(16):9775–9795

    Article  Google Scholar 

  26. Mahalanobis A, Kumar BVKV, Song S, Sims SRF, Epperson JF (1994) Unconstrained correlation filters. Appl Opt 33(17):3751–3759. https://doi.org/10.1364/AO.33.003751

    Article  Google Scholar 

  27. Nassar M, Wehbe N, Al-Bouna B (2016) K-nn classification under homomorphic encryption application on a labeled eigen faces dataset. In: 2016 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) and 15th International Symposium on Distributed Computing and Applications for Business Engineering (DCABES), pp 546–552. https://doi.org/10.1109/CSE-EUC-DCABES.2016.239. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7981485

  28. Natgunanathan I, Mehmood A, Xiang Y, Beliakov G, Yearwood J (2016) Protection of privacy in biometric data. IEEE Access 4:880–892. https://doi.org/10.1109/ACCESS.2016.2535120

    Article  Google Scholar 

  29. Osadchy M, Pinkas B, Jarrous A, Moskovich B (2010) Scifi-a system for secure face identification. IEEE Symp Secur Privac 239–254. https://doi.org/10.1109/SP.2010.39

  30. Othman A, Ross A (2013) On mixing fingerprints. IEEE Trans Inf Forensics Secur 8(1):260–267. https://doi.org/10.1109/TIFS.2012.2223676

    Article  Google Scholar 

  31. Paillier P (1999) Public-key crypto systems based on composite degree residuosity classes. In: International conference on the theory and applications of cryptographic techniques, pp 223–238. https://doi.org/10.1007/3-540-48910-X_16

  32. Patel VM, Ratha NK, Chellappa R (2015) Cancelable biometrics: a review. IEEE Signal Process Mag 32(5):54–65. https://doi.org/10.1109/MSP.2015.2434151

    Article  Google Scholar 

  33. Paul P, Gavrilova M (2012) Multimodal cancelable biometrics. In: IEEE 11th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). https://doi.org/10.1109/ICCI-CC.2012.6311208. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6302081

  34. Phillips P, Wechsler H, Huang J, Rauss P (1998) The feret database and evaluation procedure for face-recognition algorithms. Image Vis Comput 16(5):295–306. https://doi.org/10.1016/S0262-8856(97)00070-X

    Article  Google Scholar 

  35. Rahulamathavan Y, Phan R, Chambers J, Parish D (2013) Facial expression recognition in the encrypted domain based on local fisher discriminant analysis. IEEE Trans Affect Comput 4(1):83–92. https://doi.org/10.1109/T-AFFC.2012.33

    Article  Google Scholar 

  36. Rahulamathavan Y, Phan RCW, Veluru S, Cumanan K, Rajarajan M (2014) Privacy-preserving multi-class support vector machine for outsourcing the data classification in cloud. IEEE Trans Depend Secure Comput 11(5):467–479. https://doi.org/10.1109/TDSC.2013.51

    Article  Google Scholar 

  37. Ratha N, Connell J, Bolle R (2001) Enhancing security and privacy in biometrics-based authentication systems. IBM Syst J 40(3):614–634. https://doi.org/10.1147/sj.403.0614

    Article  Google Scholar 

  38. Rathgeb C, Uhl A (2011) A survey on biometric cryptosystems and cancelable biometrics. EURASIP J Inf Secur 2011(3):1–25. https://doi.org/10.1186/1687-417X-2011-3

    Google Scholar 

  39. Rathgeb C, Gomez-Barrero M, Busch C (2015) Towards cancelable multi-bio- metrics based on adaptive bloom filters: a case study on feature level fusion of face and iris. In: International Workshop on Biometrics and Forensics (IWBF), pp 1–6. https://doi.org/10.1109/IWBF.2015.7110225. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7101073

  40. Rizo-Rodrguez D, Mndez-Vzquez H, Garca-Reyes E (2012) Illumination invariant face recognition using quaternion-based correlation filters. J Math Imag Vis 45:1–12. https://doi.org/10.1007/s10851-012-0352-0

    MathSciNet  Google Scholar 

  41. Sadeghi A, Schneider T, Wehrenberg I (2009) Efficient privacy-preserving face recognition. Inf Secur Cryptol 5984:229–244. https://doi.org/10.1007/978-3-642-14423-3_16

    MathSciNet  Google Scholar 

  42. Savvides M, Kumar B, Khosla P (2002) Face verification using correlation filters.. In: 3rd IEEE automatic identification advanced technologies, pp 56–61

  43. Savvides M, Kumar BVKV, Khosla P (2004) Cancelable biometric filters for face recognition. Pattern Recogn 3:922–925. https://doi.org/10.1109/ICPR.2004.1334679

    Google Scholar 

  44. Sims SRF, Mahalanobis A (2004) Performance evaluation of quadratic correlation filters for target detection and discrimination in infrared imagery. Opt Eng 43(8):1705–1711. https://doi.org/10.1117/1.1767195

    Article  Google Scholar 

  45. Taheri M, Mozaffari S, Keshavarzi P (2015) Cancelable face verification using optical encryption and authentication. Opt Soc Amer A 32:1772–1779. https://doi.org/10.1364/JOSAA.32.001772

    Article  Google Scholar 

  46. Tarek M, Ouda O, Hamza T (2016) Robust cancellable biometrics scheme based on neural networks. IET Biomet 5(3):220–228. https://doi.org/10.1049/iet-bmt.2015.0045

    Article  Google Scholar 

  47. Veugen T (2014) Encrypted integer division and secure comparison. Int J Appl Cryptogr 3(2):166–180. https://doi.org/10.1504/IJACT.2014.062738

    Article  MathSciNet  MATH  Google Scholar 

  48. Wolf L, Hassner T., Taigman Y. (2011) Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Trans Pattern Anal Mach Intel 33(10):1978–1990. https://doi.org/10.1109/TPAMI.2010.230

    Article  Google Scholar 

  49. Yu C, Li J, Li X, Ren X, Gupta B (2017) Four-image encryption scheme based on quaternion fresnel transform, chaos and computer generated hologram. Multimed Tools Appl 1–24. https://doi.org/10.1007/s11042-017-4637-6

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran

    Motahareh Taheri, Saeed Mozaffari & Parviz Keshavarzi

Authors
  1. Motahareh Taheri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saeed Mozaffari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Parviz Keshavarzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saeed Mozaffari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Taheri, M., Mozaffari, S. & Keshavarzi, P. Face authentication in encrypted domain based on correlation filters. Multimed Tools Appl 77, 17043–17067 (2018). https://doi.org/10.1007/s11042-017-5275-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5275-8

Keywords

Search

Navigation