Design of a Solution for a Biometric Face Recognition Task
Article Sidebar
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Main Article Content
Abstract
This Biometric Face Recognition is technology which uses modern machine algorithms and techniques to identify face of specific individuals under different circumstances. Face recognition is famous as well as leading problem in machine learning. The best solution to solve this problem was to develop a Biometric Face Recognition System which can give robust solution and maximum accuracy during recognition of specific face. Different machine algorithms are deployed during development of this project in order to achieve maximum accuracy
Article Details
References
The template will number citations consecutively within brackets [1]. The sentence punctuation follows the bracket [2]. Refer simply to the reference number, as in [3]—do not use “Ref. [3]” or “reference [3]” except at the beginning of a sentence: “Reference [3] was the first...”
Number footnotes separately in superscripts. Place the actual footnote at the bottom of the column in which it was cited. Do not put footnotes in the abstract or reference list. Use letters for table foot notes.
Unless there are six authors or more give all authors’ names; do not use “et al.”. Papers that have not been published, even if they have been submitted for publication, should be cited as “unpublished” [4]. Papers that have been accepted for publication should be cited as “in press” [5]. Capitalize only the first word in a paper title, except for proper nouns and element symbols.
For papers published in translation journals, please give the English citation first, followed by the original foreign-language citation [6].
1. Techtargetcom.2019.SearchEnterpriseAI.[Online].[17 February 2019].Available from: https://searchenterpriseai.techtarget.com/definition/facial-recognition
2. Shemi P M, Ali M A, A Principal Component Analysis Method for Recognition of Human Faces: Eigenfaces Approach, International Journal of Electronics Communication and Computer Technology (IJECCT),Volume 2 Issue 3 (2012).
3. Uglov J.et al. 2007.Comparing Robustness of Pairwise and Multiclass Neural-NetworkSystems for Face Recognition.EURASIP Journal on Advances in Signal Processing.116(1),pp.1-5.
4. Coppin, B(2004).Artificial Intelligence Illuminated.(1st ed.).United States of America:Jones and Bartlett Publishers.Popova E. et al. 2018 Deep Neural Networks for Face Recognition: Pairwise Optimisation
5. Schetinin, V. (2019) ‘Principal Component Analysis for Face Recognition’ [PowerPoint presentation]. Available at: https://drive.google.com/open?id=1wotwxmLj3xBvzdvuBQHV6tr7oC3B_27kHIa7cLWacb0 (Accessed: 17February 2019).
6. Machinelearningmasterycom. 2016. Machine Learning Mastery. [Online]. [2 March 2019]. Available from: https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/
7. Epifano, C. M. & Quaglia, A., 2012. Face Recognition: Methods, Applications and Technology, s.l.: Nova Science Publishers.
8. Caudill, M., 1989. Neural Network Primer: Part I. s.l.:s.n.Dans, E., 2018. Forbes. [Online] Available at: https://www.forbes.com/sites/enriquedans/2018/06/25/facial-recognition-and-future-scenarios/#72a393811ac9[Accessed 17 February 2019].
9. Electronic Frontier Foundation, n.d. Facial Recognition. [Online]Available at: https://www.eff.org/pages/face-recognition[Accessed 13 February 2019].
10. Lehe, L., 2015. Principal Component Analysis. [Online]Available at: http://setosa.io/ev/principal-component-analysis/[Accessed 23 February 2019].1
1.MathWorks, 2019. Scaled Conjugate Gradient Backpropagation-MATLAB trainscg. [Online] Available at: https://www.mathworks.com/help/deeplearning/ref/trainscg.html[Accessed 24 February 2019].
12. Math Works, 2019. Train and Apply Multilayer Shallow Neural Networks. [Online] Available at:https://www.mathworks.com/help/deeplearning/ug/train-and-apply-multilayer-neural-networks.html[Accessed 24 February 2019].
13. Paul, L. C. & Sumam, . A. A., 2012. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET). Face Recognition Using Principal Component Analysis Method, I(9), p. 135.
14. Techtarget, 2018. Facial Recognition. [Online] Available at: https://searchenterpriseai.techtarget.com/definition/facial-recognitio[Accessed 13 February 2019].Vera-Rodriguez, R., Fierrez, J., Tome, P. & Ortega-Garcia, J., 2000. Advances in Intelligent and Soft Computing, s.l.: Springer.