Pattern Recognition and Image Analysis

, Volume 28, Issue 4, pp 813–829 | Cite as

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

  • A. H. SableEmail author
  • S. N. Talbar
Applied Problems


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).


face recognition adaptive Gaussian kernel plastic surgery EV-SIFT feature SVM and DLN classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    A. K. Sao and B. Yegnanarayana, “Face verification using template matching,” IEEE Trans. Inf. Forensics Secur. 2 (3), 636–641 (2007).CrossRefGoogle Scholar
  2. 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).MathSciNetCrossRefzbMATHGoogle Scholar
  3. 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. 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).CrossRefzbMATHGoogle Scholar
  5. 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).CrossRefGoogle Scholar
  6. 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).CrossRefGoogle Scholar
  7. 7.
    T. Inan and U. Halici, “3D face recognition with local shape descriptors,” IEEE Trans. Inf. Forensics Secur. 7 (2), 577–587 (2012).CrossRefGoogle Scholar
  8. 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).CrossRefGoogle Scholar
  9. 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).CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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. 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).CrossRefGoogle Scholar
  13. 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).CrossRefGoogle Scholar
  14. 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).CrossRefGoogle Scholar
  15. 15.
    W. W. W. Zou and P. C. Yuen, “Very low resolution face recognition problem,” IEEE Trans. Image Process. 21 (1), 327–340 (2012).MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    U. Park, Y. Tong, and A. K. Jain, “Age–invariant face recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 32 (5), 947–954 (2010).CrossRefGoogle Scholar
  17. 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).CrossRefGoogle Scholar
  18. 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. 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).CrossRefGoogle Scholar
  20. 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. 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. 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.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. 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. 25.
    H. Ouanan and M. Ouanan, “Gabor–HOG features based face recognition scheme,” TELKOMNIKA Indones. J. Electr. Eng. 15 (2), 331–335 (2015).MathSciNetGoogle Scholar
  26. 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. 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).CrossRefGoogle Scholar
  28. 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).CrossRefGoogle Scholar
  29. 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).CrossRefGoogle Scholar
  30. 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. 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).CrossRefGoogle Scholar
  32. 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. 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. 34.
    D. S. S. Al–Azzawy, “Eigenface and SIFT for gender classification,” J. Wassit Sci. Med. 5 (1), 60–76 (2012).Google Scholar
  35. 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

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.School of computational SciencesSwami Ramanand Teerth Marathwada UniversityNandedIndia
  2. 2.Department of Electronics and Telecommunication Engg.Shri Guru Gobingji Singh Institute of Engg. and Tech.NandedIndia

Personalised recommendations