Sparse Representation Approach on Surgically Altered Face Images Using Evolutionary Algorithm
Keywords:
Granular algorithm, Facial recognition, skin disorders
Abstract
Face recognition algorithms have undergone a lot of improvements in the last few years. Even so scope of further improvement is extremely high since current authentication/identification applications are limited to controlled settings, e.g., limited pose and illumination changes, with the user usually aware of being screened and collaborating in the process. Among others, pose and illumination changes are limited. This paper dealt with Multi-objective evolutionary granular algorithm with the aim of recognizing faces even after a surgical procedure, or also in the presence of any variation in appearance, texture and structural geometry.
References
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2) B. Gökberk, M. O. Irfanoglu, L. Akarun, and E. Alpaydin, “Learning the best subset of local features for face recognition,” Pattern Recognit., 2007, vol. 40, pp. 1520–1532.
3) B. Weyrauch, B. Heisele, J. Huang, and V. Blanz, “Component-based face recognition with 3d morphable models,” in Proc. Int. Conf. ComputerVision and Pattern Recognition Workshop, 2004, pp. 85–91.
4) D. G. Lowe, “Distinctive image features from scale-invariant key points,” Int. J. Comput. Vis., 2004, vol. 60, no. 2, pp. 91–110.
5) F. Li and H. Wechsler, “Robust part-based face recognition using boosting and transduction,” in Proc. Int. Conf. Biometrics: Theory, Applications, and Systems, 2007, pp. 1–5.
6) G. Aggarwal, S. Biswas, P. J. Flynn, and K. W. Bowyer, “A sparse representation approach to face matching across plastic surgery,” in Proc. Workshop on the Applications of Computer Vision, 2012, pp. 1–7.
7) H. S. Bhatt, S. Bharadwaj, R. Singh, and M. Vatsa, “On matching sketches with digital face images,” in Proc. Int. Conf. Biometrics: Theory Applications and Systems, 2010, pp. 1–7.
8) John D. Woodward, Jr., Christopher Horn, Julius Gatune, Aryn Thomas, “Biometrics, A Look at Facial Recognition,” RAND, 2003.
9) M. De Marsico, M. Nappi, D. Riccio, and H. Wechsler, “Robust face recognition after plastic surgery using local region analysis,” in Proc. Int. Conf. Image Analysis and Recognition, 2011, vol. 6754, pp. 191–200.
10) New: Google Image Search Categories,” Google Blogoscoped, May 28, 2007.
11) P. Sinha, B. Balas, Y. Ostrovsky, and R. Russell, “Face recognition by humans: Nineteen results all computer vision researchers should know about,” Proc. IEEE, . 2006, vol. 94, no. 11, pp. 1948–1962.
12) Pawan Sinha, Benjamin Balas, Yuri Ostrovsky, and Richard Russell, "Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About," Proceedings of the IEEE, Volume: 94, Issue: 11, 2006.
13) R. Campbell, M. Coleman, J.Walker, P. J. Benson, S.Wallace, J.Michelotti, and S. Baron-Cohen, “When does the inner-face advantage in familiar face recognition arise and why?,” Vis. Cognit., 1999, vol. 6, no. 2, pp. 197–216.
14) 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 Security, 2010, vol. 5, no. 3, pp. 441–448.
15) Ryan Johnson, Kevin Bonsor, "How Facial Recognition Systems Work," How Stuff Works, 2007.
16) Trina D. Russ, Mark W. Koch, Charles Q. Little, "3D Facial Recognition: A Quantitative Analysis," 38th Annual 2004 International Carnahan Conference on Security Technology, 2004.
17) W. G. Hayward, G. Rhodes, and A. Schwaninger, “An own-race advantage for components as well as configurations in face recognition,” Cognition, 2008, vol. 106, no. 2, pp. 1017–1027.
Published
2024-05-30
How to Cite
Dr.Raj Kumar.E, Dr.Bindu.SS, Anju Vikraman. V.J, Niju V S, Krishnakumar.K, Sree Raj.M.P, & Joe Jeba Rajan.K. (2024). Sparse Representation Approach on Surgically Altered Face Images Using Evolutionary Algorithm. Revista Electronica De Veterinaria, 25(1S), 2020-2027. https://doi.org/10.69980/redvet.v25i1S.2011
Section
Articles
