Skip to main content
SpringerLink
Log in

An Adaptive Entropy Based Scale Invariant Face Recognition Face Altered by Plastic Surgery

  • Applied Problems
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

Face recognition is one of the challenging problems which suffer from practical issues like the pose, expression, illumination changes, and aging. Plastic surgery is one among the issues that pose great difficulty in recognizing the faces. The literature has been reported with traditional features and classifiers for recognizing the faces after plastic surgery. This paper presents an adaptive feature descriptor and advanced classifier for plastic surgery face recognition. According to the proposed feature descriptor, firstly an adaptive Gaussian transfer function is determined to perform Adaptive Gaussian Filtering (AGF) for images. Secondly, Adaptive Entropy-based SIFT (AEV-SIFT) features are extracted from the filtered images. Unlike traditional SIFT, the proposed AEV-SIFT extracts the key points based on the entropy of the volume information of the pixel intensities. This provides the least effect on uncertain variations in the face because the entropy is the higher order statistical feature. Further, the classification is performed with variations. In the first variation, support vector machine (SVM) is used as a classifier, whereas the second variation exploits the Deep Learning Network (DLN) for recognizing the faces based on the AEV-SIFT features. The proposed method classifies the plastic surgery face images with the accuracy of 80.15%, sensitivity of 19.75% and specificity of 95%, which are obviously better than the traditional features such as SIFT, V-SIFT, and Principal Component Analysis (PCA).

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

Similar content being viewed by others

References

  1. A. K. Sao and B. Yegnanarayana, “Face verification using template matching,” IEEE Trans. Inf. Forensics Secur. 2 (3), 636–641 (2007).

    Article  Google Scholar 

  2. W. R. Schwartz, H. Guo, J. Choi, and L. S. Davis, “Face identification using large feature sets,” IEEE Trans. Image Process. 21 (4), 2245–2255 (2012).

    Article  MathSciNet  MATH  Google Scholar 

  3. M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in Proc. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Maui, HI, 1991), pp. 586–591.

    Google Scholar 

  4. J. Ruiz–del–Solar and P. Navarrete, “Eigenspace–based face recognition: A comparative study of different approaches,” IEEE Trans. Syst. Man Cybern., Part C 35 (3), 315–325 (2005).

    Article  MATH  Google Scholar 

  5. S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: a convolutional neural–network approach,” IEEE Trans. Neural Networks 8 (1), 98–113 (1997).

    Article  Google Scholar 

  6. X. He, S. Yan, Y. Hu, P. Niyogi, and H.–J. Zhang, “Face recognition using Laplacianfaces,” IEEE Trans. Pattern Anal. Mach. Intell. 27 (3), 328–340 (2005).

    Article  Google Scholar 

  7. T. Inan and U. Halici, “3D face recognition with local shape descriptors,” IEEE Trans. Inf. Forensics Secur. 7 (2), 577–587 (2012).

    Article  Google Scholar 

  8. Z. Lu, X. Jiang, and A. C. Kot, “A color channel fusion approach for face recognition,” IEEE Signal Process. Lett. 22 (11), 1839–1843 (2015).

    Article  Google Scholar 

  9. Y. Xu, X. Fang, X. Li, J. Yang, J. You, H. Liu, and S. Teng, “Data uncertainty in face recognition,” IEEE Trans. Cybern. 44 (10), 1950–1961 (2014).

    Article  Google Scholar 

  10. A. K. Jain, B. Klare, and U. Park, “Face recognition: Some challenges in forensics,” in Face and Gesture 2011: Proc. 2011 IEEE Int. Conf. on Automatic Face and Gesture Recognition and Workshops (FG 2011) (Santa Barbara, CA, 2011), pp. 726–733.

    Chapter  Google Scholar 

  11. C. C. Chude–Olisah, G. B. Sulong, U. A. K. Chude–Okonkwo, and S. Z. M. Hashim, “Edge–based representation and recognition for surgically altered face images,” in Proc. 2013 7th Int. Conf. on Signal Processing and Communication Systems (ICSPCS) (Carrara, Australia, 2013), pp. 1–7.

    Google Scholar 

  12. N. Kohli, D. Yadav, and A. Noore, “Multiple projective dictionary learning to detect plastic surgery for face verification,” IEEE Access 3, 2572–2580 (2015).

    Article  Google Scholar 

  13. P. Sharma, R. N. Yadav, and K. V. Arya, “Pose–invariant face recognition using curvelet neural network,” IET Biometrics 3 (3), 128–138 (2014).

    Article  Google Scholar 

  14. S.–H. Lee, D.–J. Kim, and J.–H. Cho, “Illumination–robust face recognition system based on differential components,” IEEE Trans. Consum. Electron. 58 (3), 963–970 (2012).

    Article  Google Scholar 

  15. W. W. W. Zou and P. C. Yuen, “Very low resolution face recognition problem,” IEEE Trans. Image Process. 21 (1), 327–340 (2012).

    Article  MathSciNet  MATH  Google Scholar 

  16. U. Park, Y. Tong, and A. K. Jain, “Age–invariant face recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 32 (5), 947–954 (2010).

    Article  Google Scholar 

  17. S. P. Mudunuri and S. Biswas, “Low resolution face recognition across variations in pose and illumination,” IEEE Trans. Pattern Anal. Mach. Intell. 38 (5), 1034–1040 (2016).

    Article  Google Scholar 

  18. R. Singh, M. Vatsa, and A. Noore, “Effect of plastic surgery on face recognition: A preliminary study,” in Proc. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (Miami, FL, 2009), pp. 72–77.

    Google Scholar 

  19. R. Singh, M. Vatsa, H. S. Bhatt, S. Bharadwaj, A. Noore, and S. S. Nooreyezdan, “Plastic surgery: A new dimension to face recognition,” IEEE Trans. Inf. Forensics Secur. 5 (3), 441–448 (2010).

    Article  Google Scholar 

  20. X. Liu, S. Shan, and X. Chen, “Face recognition after plastic surgery: A comprehensive study,” in Computer Vision–ACCV 2012, Proc. 11th Asian Conf. on Computer Vision, Part II, Ed. by K. M. Lee, Y. Matsushita, J. M. Rehg, and Z. Hu, Lecture Notes in Computer Science (Springer, Berlin, Heidelberg, 2012), Vol. 7725, pp. 565–576.

    Google Scholar 

  21. M. De Marsico, M. Nappi, D. Riccio, and H. Wechsler, “Robust face recognition after plastic surgery using local region analysis,” in Image Analysis and Recognition, Proc. 8th Int. Conf. ICIAR 2011, Part II, Ed. by M. Kamel and A. Campilho, Lecture Notes in Computer Science (Springer, Berlin, Heidelberg, 2011), Vol. 6754, pp. 191–200.

    Google Scholar 

  22. N. S. Lakshmiprabha and S. Majumder, “Face recognition system invariant to plastic surgery,” in Proc. 2012 12th Int. Conf. on Intelligent Systems Design and Applications (ISDA) (Kochi, India, 2012), pp. 258–263.

    Chapter  Google Scholar 

  23. A. R. Gulhane, S. A. Ladhake, and S. B. Kasturiwala, “A review on surgically altered face images recognition using multimodal bio–metric features,” in Proc. 2015 2nd Int. Conf. on Electronics and Communication Systems (ICECS) (Coimbatore, India, 2015), pp. 1168–1171.

    Chapter  Google Scholar 

  24. C. Chude–Olisah, G. Sulong, U. A. K. Chude–Okonkwo, and S. Z. M. Hashim, “Face recognition via edge–based Gabor feature representation for plastic surgery–altered images,” EURASIP J. Adv. Signal Process. 2014 (102), 1–15 (2014).

    Google Scholar 

  25. H. Ouanan and M. Ouanan, “Gabor–HOG features based face recognition scheme,” TELKOMNIKA Indones. J. Electr. Eng. 15 (2), 331–335 (2015).

    MathSciNet  Google Scholar 

  26. M. I. Ouloul, Z. Moutakki, K. Afdel, and A. Amghar, “An efficient face recognition using SIFT descriptor in RGB–D images,” Int. J. Electr. Comput. Eng. (IJECE) 5 (6), 1227–1233 (2015).

    Google Scholar 

  27. H. S. Bhatt, S. Bharadwaj, R. Singh, and M. Vatsa, “Recognizing surgically altered face images using multiobjective evolutionary algorithm,” IEEE Trans. Inf. Forensics Secur. 8 (1), 89–100 (2013).

    Article  Google Scholar 

  28. A. S. O. Ali, V. Sagayan, A. Malik, and A. Aziz, “Proposed face recognition system after plastic surgery,” IET Comput. Vision 10 (5), 342–348 (2016).

    Article  Google Scholar 

  29. M. De Marsico, M. Nappi, D. Riccio, and H. Wechsler, “Robust face recognition after plastic surgery using region–based approaches,” Pattern Recogn. 48 (4), 1261–1276 (2015).

    Article  Google Scholar 

  30. R. Tavoli, E. Kozegar, M. Shojafar, H. M., Soleimani and Z. Pooranian, “Weighted PCA for improving Document Image Retrieval System based on keyword spotting accuracy,” in Proc. 2013 36th Int. Conf. on Telecommunications and Signal Processing (TSP) (Rome, Italy, 2013), pp. 773–777.

    Google Scholar 

  31. D. Petkovic, S. Shamshirband, N. B. Anuar, A. Q. Md. Sabri, Z. B. A. Rahman, and N. D. Pavlovic, “Input displacement neuro–fuzzy control and object recognition by compliant multi–fingered passively adaptive robotic gripper,” J. Intell. Robot. Syst. 82 (2), 177–187 (2016).

    Article  Google Scholar 

  32. S. Agaian, M. Madhukar, and A. T. Chronopoulos, “A new acute leukaemia–automated classification system,” Comput. Methods Biomech. Biomed. Eng.: Imaging and Visualization 6 (3), 303–314 (2018).

    Google Scholar 

  33. M. Madhukar, S. Agaian, and A. T. Chronopoulos, “Deterministic model for Acute MyelogeNous Leukemia classification,” in Proc. 2012 IEEE Int. Conf. on Systems, Man and Cybernetics (SMC) (Seoul, South Korea, 2012), pp. 433–438.

    Google Scholar 

  34. D. S. S. Al–Azzawy, “Eigenface and SIFT for gender classification,” J. Wassit Sci. Med. 5 (1), 60–76 (2012).

    Google Scholar 

  35. C. Geng and X. Jiang, “Face Recognition Using SIFT Features,” in Proc. 2009 16th IEEE Int. Conf. on Image Processing (ICIP) (Cairo, Egypt, 2009), pp. 3313–3316.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of computational Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra, India

    A. H. Sable

  2. Department of Electronics and Telecommunication Engg., Shri Guru Gobingji Singh Institute of Engg. and Tech., Nanded, (M.S), India

    S. N. Talbar

Authors
  1. A. H. Sable
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. N. Talbar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. H. Sable.

Additional information

The article is published in the original.

Archana H. Sable, she received ME degree in computer Science and engineering from M.G.M’s College of Engg., SRTM University, Nanded. Her research interests are Image processing, Pattern recognizing and Computer vision. She is doing PhD (Thesis) in Computer Engg. from SRTM University, Nanded.

Sanjay N. Talbar received his B.E and M.E degrees from SGGS Institute of Technology, Nanded, India in 1985 and 1990, respectively. He obtained his PhD from SRTM University, Nanded, India in 2000. He received the “Young Scientist Award” by URSI, Italy in 2003. He had Collaborative research programme at Cardiff University Wales, UK. Presently he is working as Professor and Head, Department of Electronics and Telecommunication Engg., SGGS Institute of Engineering and Technology Nanded, India. He has published 50 journal papers, 10 books, and more than 125 papers in referred National as well as International Conferences. His research interests includes Image processing, Medical Image processing, Multimedia Computing and Embedded System Design. He is a member of IEEE, IET, IETE, AMPI, ISTE, and worked on many prestigious committees in academic field of India.

Haricharan Amarsing Dhirbasi, he received B.Sc (Mathematics), M.Sc (Mathematics), M. Phil. (Mathematics) from SRTM University, Nanded. He is SET (Mathematics) qualified. Hi’s research interests is in Study and application of Integral Transform in Image processing. He is doing PhD (Mathematics) from SRTM University, Nanded.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sable, A.H., Talbar, S.N. An Adaptive Entropy Based Scale Invariant Face Recognition Face Altered by Plastic Surgery. Pattern Recognit. Image Anal. 28, 813–829 (2018). https://doi.org/10.1134/S1054661818040041

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1054661818040041

Keywords

Search

Navigation