EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 44 (2022) ITM Web Conf., 44 (2022) 03046 References
Open Access
Issue
ITM Web Conf.
Volume 44, 2022
International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
Article Number 03046
Number of page(s) 8
Section Computing
DOI https://doi.org/10.1051/itmconf/20224403046
Published online 05 May 2022
  1. Nurulhuda Ismail, Mas Idayu MD. SABRI Department of System and Computer Technology (Multimedia), Faculty of Computer Science and Information Technology, University of Malaya, 50603 LembahPantai, Kuala Lumpur, MALAYSIA, Page 1–11 [Google Scholar]
  2. Umamaheswari, K., Sumathi, S., Sivanandam, S.N., and Ponson, T. (2007). Artificial Neural Network Based Image Data Mining with Hybrid Recognizers. International Journal of Soft Computing Applications. Issue 1, Page. 27–49. [Google Scholar]
  3. Praseeda, L. and Sasikumar, M. (2008). RBF Based Face Recognition and Expression Analysis. In Proceedings of World Academy of Science, Engineering and Technology. Vol. 35. [Google Scholar]
  4. Mohamed, D. (2008). A Simple Face Recognition Algorithm using Eigeneyes and a Class-Dependent PCA Implementation. International Journal of Soft Computing. vol. 3, no. 6, Page. 438–449. [MathSciNet] [Google Scholar]
  5. Manjunath, B.S. and Chellappa, R. (1993). A Unified Approach to Boundary Perception: Edges, Textures and Illusory Contours. In Proceedings of IEEE Transactions Neural Networks. Vol. 4, No.1, Page. 96–107. [CrossRef] [Google Scholar]
  6. Preeti Chatterjee, Subhadeep Jana & Souradeep Ghosh, Global Journal of Computer Science and Technology: G Interdisciplinary Volume 21 Issue 2 Version 1.0 Year 2021 [Google Scholar]
  7. Umamaheswari, K., Sumathi, S., Sivanandam, S.N., and Ponson, T. (2007). Artificial Neural Network Based Image Data Mining with Hybrid Recognizers. International Journal of Soft Computing Applications. Issue 1, Page. 27–49. [Google Scholar]
  8. K. Delac and M. Grgic, “A survey of biometric recognition methods,” in 46th International Symposium Electronics in Marine, vol. 46, pp. 16–18, 2004. [Google Scholar]
  9. Singh, S.K., Chauhan, D.S., Vatsa, and Singh, R. (2003). A Robust Skin Colour Based Face Detection Algorithm. Tamkang Journal of Science and Engineering. vol. 6, no. 4, Page. 227–234. [Google Scholar]
  10. Sasha Callaway Jeffrey Cheng Alexander Contratti, David Fu Harshika Gelivi Jacob Wachulec, Sonia Purohit, Comparative Analysis of Image Processing Algorithms for Airport Security, New Jersey’s Governor’s School of Engineering and Technology, July 18, 2020. [Google Scholar]
  11. Turk, M., and Pentland, A. (1991). Face Recognition Using Eigenfaces. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Page. 586–591. [Google Scholar]
  12. Michael Jones, MA 02139 Cambridge, MA 02142, Mitsubishi Electric Research Labs Compaq CRL 201 Broadway, Paul Viola, 8th FL One Cambridge Center Cambridge, Rapid Object Detection using a Boosted Cascade of Simple Features. [Google Scholar]
  13. Kelvin Salton Do Prado, Facial Recognition: Understanding LBPH Algorithm, November 11, 2017. [Google Scholar]
  14. J. Brownlee. “How to Control Neural Network Model Capacity with Nodes and Layers.” Machine Learning Mastery, 10 Jan. 2020. [Google Scholar]
  15. Rowley, H., Baluja, S., and Kanade, T. (1998). Neural Network- Based Face Detection. In Proceedings of IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 1, no. 20, Page.23–28. [CrossRef] [Google Scholar]
  16. W. Koehrsen. “Beyond Accuracy: Precision and Recall.” Medium, Towards Data Science, 10 Mar. 2018. [Google Scholar]
  17. Peter N. Belhumeur, Joao P. Hespanha, and David J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 19, NO. 7, JULY 1997. [Google Scholar]
  18. Amir Dirin, Nicolas Delbiaggio, Janne Kauttonen Haaga-Helia University of Applied Science, Helsinki, Finland, Comparisons of Facial Recognition Algorithms Through a Case Study Application. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.