Face Granulation Scheme for Identity Proving After Plastic Surgery

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2014 by IJETT Journal
Volume-13 Number-3
Year of Publication : 2014
Authors : Miphy Tom , Jubilant J Kizhakkethottam


Miphy Tom , Jubilant J Kizhakkethottam. "Face Granulation Scheme for Identity Proving After Plastic Surgery", International Journal of Engineering Trends and Technology (IJETT), V13(3),129-133 July 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group


The importance of biometric authentication is increasing rapidly because it verifies the claimed user identity. There are different types of biometrics available such as finger print, facial scan, retinal scan, voice print. From these, face is one of the most commonly used biometric. Hence the development of face recognition system seems to be useful. There are many techniques people use to evade their identification. Plastic surgery is one of them. Plastic surgery is a surgical procedure to correct the facial anomalies or to improve the appearance of the face. Matching of images before and after the plastic surgery process is the difficult task for automatic face recognition systems because of the wide variations created due to plastic surgery. Here propose a method to match before and after surgery images so one can prove the identity. For this image is divided in to different granules and features are extracted using SIFT and Efficient LBP to get different information’s from the face granules. The features are selected using SWARM Optimization feature selection algorithm


[1] Singh, R., Vatsa, M., Bhatt, H., Bharadwaj, S., Noore, A., Nooreyezdan, S Plastic surgery: a new dimension to face recognition Information Forensics and Security,IEEE Transactions on 5 (2010) 441-448.
[2] Himanshu S. Bhatt, Samarth Bharadwaj, Richa Singh, and Mayank Vatsa “Recognizing Surgically Altered Face Images Using Miobjective Evolutionary Algorithm” Ieee Transactions On Information Forensics And Security, Vol. 8, No. 1, January 2013
[3] T. Ahonen, A. Hadid, and M. Pietik¨ainen, “Face recognition with local binary patterns,” in Proc. Euro. Conf. Comput. Vis., 2004, pp. 469–481.
[4] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, 2004.
[5] J. Kennedy and R. Eberhart, Particle swarm optimization, Proc. IEEE International Conference on Neural Networks, pp. 1942-1948, 1995.
[6] R. C. Eberhart and Y. Shi, Comparison between Genetic Algorithms and Particle Swarm Optimization, Proc. 7th international Conference on Evolutionary Programming, pp. 611- 616, 1998.
[7] X. Li, A Non-dominated Sorting Particle Swarm Optimizer for Multi-objective Optimization, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2003), ser. Lecture Notes in Computer Science, vol. 2723. Springer, 2003, pp. 37“48.
[8] H. A. Firpi and E. Goodman, “Swarmed feature selection,” in Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop. Washington, DC, USA: IEEE Computer Society, 2004, pp. 112–118.
[9] B. Heisele, P. Ho, J. Wu, and T. Poggio, “Face recognition: Component-based versus global approaches,” Comput. Vis. Image Understand., vol. 91, pp. 6–21, 2003.
[10] Beema K.K, S. Shobana,” A New Dimensional Approach towards Fraps-Face Recognition after Plastic Surgery” International Journal of Innovative Research in Computer and Communication Engineering Vol.2, Special Issue 1, March 2014

Plastic surgery, SIFT, Efficient LBP, SWARM Optimization Algorithm.