Comparative Study of Different Face Recognition Dataset on Machine Learning Algorithm

Abstract

AI computations can sort out how to perform basic tasks by summarizing from representations. This examination targets looking at changed calculations utilized in AI. AI can be both experience and clarification based learning. In this examination, the most mainstream calculations were utilized like (LBPH), (SVM), (LDA), and (KNN). The datasets were utilized to check the viability of calculations. Near assessment of the classifiers shows that KNN is better than different techniques with high exactness. 

Country : India

1 Thummalagunta Aswani

  1. Assistant Professor, Department of Computer Science And Engineering, Malla Reddy College of Engineering for Women, Hyderabad -500100, Telangana, India

IRJIET, Volume 2, Issue 6, August 2018 pp. 39-43

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References

  1. Suresh, S.Prem kumar, “Attendance Monitoring System By LBPH Algorithm”, International Journal of Advanced Science and Technology Vol. 29, No. 5, (2020), pp. 3602 - 3606.
  2. Sushma Jaiswal, Dr. (Smt.) Sarita Singh Bhadauria, Dr. Rakesh Singh Jadon3, “Comparison Between Face Recognition Algorithm-Eigenfaces, Fisherfaces And Elastic Bunch Graph Matching,” Journal of Global Research in Computer Science , Volume 2, No. 7, July 2011.
  3. Suman Kumar Bhattacharyya, Kumar Rahul,“Face Recognition by Linear Discriminant Analysis,” unpublished.
  4. G. J. Alvarado, W. Pedrycz, M. Reformat, Keun-Chang Kwak “Deterioration of visual information in face classification using Eigenfaces and Fisherfaces,” Machine Vision and Applications, Vol.17, No. 1, pp. 68-82. Springer- Verlag 2006.
  5. Alfalou and C. Brosseau, “A New Robus Discriminating Method for Face Recognition Base Correlation Technique and Independent Compone Analysis Model,” Optics Letters 36 2011 645-647.
  6. Kapil Sethi, Ankit Gupta, Gaurav Gupta, Varun Jaiswal, “Comparative Analysis of Machine Learning Algorithms on Different Datasets”, Circulation in Computer Science International Conference on Innovations in Computing (ICIC 2017), pp:87-91.
  7. Colmenarez A.J., Huang T.S. (1998) Face Detection and Recognition. In: Wechsler H., Phillips P.J., Bruce V., Soulié F.F., Huang T.S. (eds) Face Recognition. NATO ASI Series (Series F: Computer and Systems Sciences), vol 163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72201-1_9.
  8. Rakesh Saini, Abhishek Saini , Deepak Agarwal , “Analysis of Different Face Recognition Algorithms”, International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 www.ijert.orgIJERTV3IS111235 (This work is licensed under a Creative Commons Attribution 4.0 International License.)Vol. 3 Issue 11, November-2014.
  9. Madan Lal, Kamlesh Kumar, Rafaqat Hussain Arain, Abdullah Maitlo, Sadaquat Ali Ruk, Hidayatullah Shaikh, “Study of Face Recognition Techniques: A Survey”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 6, 2018.
  10. Aftab Ahmed, Farah Deeba, Fayaz Ali, Jiandong Guo, “LBPH Based Improved Face Recognition At Low Resolution”, https://www.researchgate.net/publication/327980768_LBPH_Based_Improved_Face_Recognition_At_Low_Resolution