Issue:An intuitionistic fuzzy facial recognition approach by eigenvalues

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http://ifigenia.org/wiki/issue:nifs/23/2/111-118

Title of paper: An intuitionistic fuzzy facial recognition approach by eigenvalues Author(s): Todor Petkov Laboratory of Intelligent Systems, University “Prof. Dr. Assen Zlatarov”, 1 Prof. Asen Zlatarov University, Bourgas-8010, Bulgaria todor_petkov@btu.bg Todor Kostadinov Laboratory of Intelligent Systems, University “Prof. Dr. Assen Zlatarov”, 1 Prof. Asen Zlatarov University, Bourgas-8010, Bulgaria kostadinov.todor@btu.bg Sotir Sotirov Laboratory of Intelligent Systems, University “Prof. Dr. Assen Zlatarov”, 1 Prof. Asen Zlatarov University, Bourgas-8010, Bulgaria ssotirov@btu.bg Maciej Krawczak Systems Research lnstitute - Polish Academy of Sciences, ul. Newelska 6, OL-447 Warsaw, Poland krawczak@ibspan.waw.pl Presented at: 21st International Conference on Intuitionistic Fuzzy Sets, 22–23 May 2017, Burgas, Bulgaria Published in: "Notes on Intuitionistic Fuzzy Sets", Volume 23, 2017, Number 2, pages 111—118 Download:  PDF (157 Kb  Kb, File info) Abstract: In the present paper a facial recognition approach using a reduced set of image values as a training vector is presented. The image simplification is performed by using the calculated Eigenvector of an image to train a neural network. It results lower processing times for rough image recognition. This approach is ideal for rough facial acquisition in dynamic background where it can be used as an early detection system. The degree of coincidence is stated by an intuitionistic fuzzy estimation. To verify the approach correctness an experiment involving variety of tests over human and non-human on objects of is carried out. Keywords: Intuitionistic fuzzy sets, Facial recognition, Image recognition, Eigenvalues, Neural networks. AMS Classification: 03E72. References: Gavrilova, M. L., Ahmed, F., Azam, S., Paul, P. P., Rahman, W., Sultana, M., & Zohra, F. T. (2016). Emerging trends in security system design using the concept of social behavioural biometrics, Information Fusion for Cyber Security Analytics, Studies in Computational Intelligence, Volume 691, 229–251, Springer. Hagan, M., Demuth, H., & Beale, M. (1996). Neural Network Design. PWS Publishing, Boston. Haykin, S. (1994). Neural Networks: A Comprehensive Foundation. Macmillan, New York. Chemsi, M., Agbossou, K., & Cardenas, A. (2016). Neural network backpropagation algorithm control for PEM fuel cell in residential applications, 2016 IEEE Electrical Power and Energy Conference (EPEC), 12-14 Oct. 2016, Ottawa, ON, Canada. Deschrijver, G., Cornelis, C., & Kerre, E. E. (2004). On the Representation of Intuitionistic Fuzzy t-Norms and t-Conorms, IEEE Trans. on Fuzzy Systems, 12(1), 45–61. Murphy, T., & Schultz, R. (2014). A Viola-Jones based hybrid face detection framework, Proceedings of SPIE, The International Society for Optical Engineering, Volume 9025. Rashidan, M. A., Mustafah, Y. M., Abidin, Z. Z., Zainuddin, N. A., & Aziz, N. N. A. (2014). Analysis of artificial neural network and Viola-Jones algorithm based moving object detection, Int. Conf. on Computer and Communication Engineering (ICCCE), 251–254. Atanassov, K. (2012). On Intuitionistic Fuzzy Sets Theory. Springer, Berlin. Citations: Issue:Generalized net model of a biometric authentication system based on palm geometry and palm vein matching using intuitionistic fuzzy evaluations ‎ (← links) The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.