RECOGNITION OF FACE PATTERNS USING SINGULAR VALUE DECOMPOSITION TO IDENTIFY FRIENDSHIP RELATIONSHIP
Main Article Content
Buhori Muslim
Eva Susilawati
Lia Saniah
A system to recognize the faces of family members is the next generation of the system based on biometrics that we commonly encounter using facial patterns and characteristics as identification objects. This family member recognition system uses existing facial patterns in the database system as storage, then does a comparison with the tested image. The facial pattern recognition system has some problems, for example, difficulty in recognizing objects with different lighting levels in the process of taking the picture. To overcome the obstacles that occur due to variations in light levels was developed software by applying the singular value decomposition (SDV) method. The result of In this study, the application of facial pattern recognition is thought to have a relationship
kinship that is useful for identifying someone who is in the test image as well as displays a person's identity if a match is found with the sample on the application. In this study, the samples used were 100 facial images and 50 facial images of random humans, the resulting level of accuracy of this software is 92.8769% in recognizing patterns of kinship face, 60.1775% in matching the test image with the existing sample on the basis application data and 90.9567% when determining the identity of someone who is the same as databases.
Akter, R. et.al. 2016. “Accuracy analysis of recommendation system using singular value decomposition,” in 2016 19th International Conference on Computer and Information Technology (ICCIT), Dhaka, 2016, pp. 405-408.
Bobadilla, J F. et.al. 2013. “Recommender systems survey,” Knowledge-Based Sytems, vol. 46, pp. 109-132.
Baker, Kirk. 2013. Singular Value Decomposition Tutorial (Revised Edition).
Guan, X. C.-T. Li, et.al. 2017. "Matrix Factorization With Rating Completion: An Enhanced SVD Model for Collaborative Filtering Recommender Systems,” IEEE Access, vol. 5, pp. 27668-27678.
James, D. Witten, et, al. 2021. An Introduction to Statistical Learning: with Applications in R, 2nd ed., New York: Springer, 2021.
Kumar. 2021. “Singular Value Decomposition (SVD) & Its Application In Recommender System,” Analytics India Magazine. [Online]. Available: Singular Value Decomposition (SVD) & Its Application In Recommender System.
Kuhn, M, et al. 2018. Applied Predictive Modeling, 1st ed. 2013, Corr. 2nd printing 2018 Edition., New York: Springer, 2018.
Mulyanto, Edi. 2007. “Catatan Kuliah Pengolahan Citra”, Teknik Informatika Udinus.
Muslim, B. 2018. Quantitative Risk Analysis of Asset Information Technology at STT Pagaralam. Prosiding STTA Yogyakarta (Senatik 2018). Jilid 1. pp 501-509.
Qing, Y.X. 2014. “An Intelligent E-Commerce Recommendation Algorithm Based on Collaborative Filtering Technology,” in 2014 7th International Conference on Intelligent Computation Technology and Automation, Changsha, pp. 80-83.
Rafael C. Gonzalez and Richard E. Woods. 2002. “Digital Image Processing”, Addison- Wesley Publishing.
Wang, X and C. Wang. 2017. “Recommendation system of e-commerce based on improved collaborative filtering algorithm,” in 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, pp. 332-335.
Wu, Q. Liu, and S. W. Luo. 2008. “Clustering Technology Application in e-Commerce Recommendation System,” in 2008 International Conference on Management of e-Commerce and e-Government, Nanchang, 2008, pp. 200-203.
Waskom, M. 2021. “seaborn: statistical data visualization,” Seaborn. [Online]. Available: https://seaborn.pydata.org/
Xia, J. 2016. “E-Commerce Product Recommendation Method Based on Collaborative Filtering Technology,” in 2016 International Conference on Smart Grid and Electrical Automation (ICSGEA), Zhangjiajie, 2016, pp. 90-93.
Zeng, Guoliang. 2006. “Face Recognition with Singular Value Decomposition”. CISSE Proceeding.
Zhou, F. Wan, et.al. 2020. “Model and Implementation of E-commerce Recommendation System Based on User Clustering,” in 2020 International Conference on Advance in Ambient Computing and Intelligence (ICAACI), Ottawa, pp. 197-200.
