SCRAMBLING FACE IMAGES FOR PRIVACY PROTECTION USING ARNOLD TRANSFORM

  • Department of Electronics and Communication Engineering, SCAD College of Engineering and Technology, Cheranmahadevi. India.
Crossref Cited-by Linking logo

  • Abstract
  • Keywords
  • References
  • Cite This Article as
  • Corresponding Author

Secure image communication is becoming increasingly important due to theft and manipulation of its content. Law enforcement agents may find it increasingly difficult to stay afloat above the ill intentions of hackers. To deal with this problem, facial image scrambling technique appears as a solution for privacy related applications. This project proposes scrambling face images for the purpose of privacy protection using Arnold transform. In the proposed method, the facial features are extracted using Kernel Principle Component Analysis and the feature selection method is used to select important features for classification. This paper presents a system that uses Arnold transform to scramble an image. The number of times the transform is applied depends on a secret message expressed in a higher base. Then kernel representation based classifier is used for classifying the facial images. This kernel classifier transforms the scrambled image data into a three dimensions space through the mapping. The experiments show that the proposed face verification system identifies ID Code of the authorized person and solves the challenging tests in the scrambled domain.

  1. Cootes T. F., Edwards G. J., and Taylor C. J., (2001), “Active appearance models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, pp. 681–685.
  2. Draper B. A., Baek K., Bartlett M., and Beveridge J., (2003), “Recognizing faces with PCA and ICA,” Vis. Image Und., vol. 91, nos. 1–2, pp. 115–137.
  3. Erdélyi A., Barát. T., Valet P., Winkler T., and Rinner B., (2014), “Adaptive Cartooning for Privacy Protection in Camera Networks,” in 11th IEEE Int. Conf. Adv. Video Signal Based Surveill., pp. 44–49.
  4. He X., Yan S., Hu Y., Niyogi P., and Zhang H. J., (2005), “Face recognition using Laplacianfaces,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 3, pp. 328–340
  5. Honda T., Murakami Y., Yanagihara Y., Kumaki T., and Fujino. T.,(2013), “Hierarchical image-scrambling method with scramble-level controllability for privacy protection,” in IEEE 56th Int. Midwest Symp. Circuits Syst. (MWSCAS), pp. 1371–1374.
  6. Jiang R. and Crookes D., (2014), “Deep salience: Visual salience modeling via deep belief propagation,” in AAAI, Quebec City, QC, Canada, pp. 2773–2779.
  7. Jiang R., Maadeed S. A., Bouridane A., Crookes D., and Celebi M. E., (2016), “Face recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels,” IEEE Trans. on Inform. Forensics and Security, vol. 11, no. 8, , pp. 1807-1817.
  8. Lin Y. Y., Liu T. L., and Fuh C. S., (2011), “Multiple kernel learning for dimensionality reduction,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 6, pp. 1147–1160.
  9. McDuff D., Kaliouby R. E., and Picard R. W., (2012), “Crowdsourcing facial responses to online videos,” IEEE Trans. Affective Comput., vol. 3, no. 4, pp. 456–468.
  10. Melle A. and Dugelay J. L., (2014), “Scrambling faces for privacy protection using background self-similarities,” in IEEE Int. Conf. Image Process. (ICIP), pp. 6046–6050.
  11. Perakis .P, Passalis G., Theoharis T., and Kakadiaris I. A., (2013), “3D facial landmark detection under large yaw and expression variations,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 7, pp. 1552–1564.
  12. Rahulamathavan Y., Phan R. C. W., Chambers J. A., and Parish D. J., (2013), “Facial expression recognition in the encrypted domain based on local Fisher discriminant analysis,” IEEE Trans. Affective Comput., vol. 4, no. 1, pp. 83–92.
  13. Sim T., Baker S., and Bsat M., (2002), “The CMU pose, illumination, and expression (PIE) database,” in IEEE Int. Conf. Autom. Face Gesture Recognit., pp. 46–51.
  14. Taheri S., Patel V. M., and Chellappa R., (2013), “Component-based recognition of faces and facial expressions,” IEEE Trans. Affective Comput., vol. 4, no. 4, pp. 360–371.
  15. Timotius I.K., Setyawan I., and Febrianto A., (2010), “Face Recognition between Two Person using Kernel Principal Component Analysis and Support Vector Machines”, International Journal on Electrical Engineering and Informatics, Vol. 2, no. 1,pp-53-61.
  16. Wang D., Chang C., Liu Y, Song G., and Liu Y., (2015), “Digital Image Scrambling Algorithm Based on Chaotic Sequence and Decomposition and Recombination of Pixel Values” International Journal of Network Security, Vol.17, No.3, PP.322-327.
  17. Wright J., Yang A. Y., Ganesh A., Sastry S. S, and Ma Y., (2009), “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210–227.
  18. Yale database [Online]. Available: http://cvc.yale.edu.

[Abirami. P. (2017); SCRAMBLING FACE IMAGES FOR PRIVACY PROTECTION USING ARNOLD TRANSFORM Int. J. of Adv. Res. 5 (Jan). 1297-1303] (ISSN 2320-5407). www.journalijar.com

ABIRAMI.P
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING, SCAD COLLEGE OF ENGINEERING AND TECHONOLOGY, CHERANMAHADEVI.

DOI:

Article DOI: 10.21474/IJAR01/2886      
DOI URL: https://dx.doi.org/10.21474/IJAR01/2886

Download Full Paper

Download PDF No. of Downloads: 22 | No. of Views: 142