Issue:An intuitionistic fuzzy facial recognition approach by eigenvalues - Revision history

Vassia Atanassova: Text replacement - ""Notes on IFS", Volume" to ""Notes on Intuitionistic Fuzzy Sets", Volume"

2024-08-28T15:20:56Z

Text replacement - ""Notes on IFS", Volume" to ""Notes on Intuitionistic Fuzzy Sets", Volume"

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	\| conference = 21st International Conference on Intuitionistic Fuzzy Sets, 22–23 May 2017, Burgas, Bulgaria		\| conference = 21st International Conference on Intuitionistic Fuzzy Sets, 22–23 May 2017, Burgas, Bulgaria
	\| issue = [[Notes on Intuitionistic Fuzzy Sets/23/2\|"Notes on ~~IFS~~", Volume 23, 2017, Number 2]], pages 111—118		\| issue = [[Notes on Intuitionistic Fuzzy Sets/23/2\|"Notes on Intuitionistic Fuzzy Sets", Volume 23, 2017, Number 2]], pages 111—118
	\| file = NIFS-23-2-111-118.pdf		\| file = NIFS-23-2-111-118.pdf
	\| format = PDF		\| format = PDF

Peter Vassilev at 13:36, 18 May 2017

2017-05-18T13:36:54Z

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	\| format = PDF		\| format = PDF
	\| size = 157 Kb		\| size = 157 Kb
	\| 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		\| 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.
	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.		\| keywords = Intuitionistic fuzzy sets, Facial recognition, Image recognition, Eigenvalues, Neural networks.
	\| ams = 03E72.		\| ams = 03E72.

Peter Vassilev: Created page with "{{PAGENAME}} {{PAGENAME}} {{PAGENAME}}..."

2017-05-18T13:36:19Z

Created page with "{{PAGENAME}} {{PAGENAME}} {{PAGENAME}}..."

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[[Category:Publications on intuitionistic fuzzy sets|{{PAGENAME}}]]
[[Category:Publications in Notes on IFS|{{PAGENAME}}]]
[[Category:Publications in 2017 year|{{PAGENAME}}]]
{{issue/title
| title = An intuitionistic fuzzy facial recognition approach by eigenvalues
| shortcut = nifs/23/2/111-118
}}

{{issue/author
| author = Todor Petkov
| institution = Laboratory of Intelligent Systems, University “Prof. Dr. Assen Zlatarov”
| address = 1 Prof. Asen Zlatarov University, Bourgas-8010, Bulgaria
| email-before-at = todor_petkov
| email-after-at = btu.bg
}}
{{issue/author
| author = Todor Kostadinov
| institution = Laboratory of Intelligent Systems, University “Prof. Dr. Assen Zlatarov”
| address = 1 Prof. Asen Zlatarov University, Bourgas-8010, Bulgaria
| email-before-at = kostadinov.todor
| email-after-at = btu.bg
}}
{{issue/author
| author = Sotir Sotirov
| institution = Laboratory of Intelligent Systems, University “Prof. Dr. Assen Zlatarov”
| address = 1 Prof. Asen Zlatarov University, Bourgas-8010, Bulgaria
| email-before-at = ssotirov
| email-after-at = btu.bg
}}
{{issue/author
| author = Maciej Krawczak
| institution = Systems Research lnstitute - Polish Academy of Sciences
| address = ul. Newelska 6, OL-447 Warsaw, Poland
| email-before-at = krawczak
| email-after-at = ibspan.waw.pl
}}

{{issue/data
| conference = 21st International Conference on Intuitionistic Fuzzy Sets, 22–23 May 2017, Burgas, Bulgaria
| issue = [[Notes on Intuitionistic Fuzzy Sets/23/2|"Notes on IFS", Volume 23, 2017, Number 2]], pages 111—118
| file = NIFS-23-2-111-118.pdf
| format = PDF
| size = 157 Kb
| 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 = 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 =
| see-also =
}}