Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 08, August 2024

Call for Paper

Vol 11, Issue 08, August 2024

Indexing

Real-Time Attendance system based on video surveillance system

Author : K. Susheel Kumar 1 Maneet Singh 2

PDF

Date of Publication :22nd February 2018

Abstract: This paper presents the real-time system for student’s attending for a large amount of made dataset for a whole class face. The real-time attendance system task is very difficult to evaluate the attendance by recognizes the face in different the environment. Human face image in term of challenges of pose and expression to identify the person is a huge variation of the system. and this is used to avoid the proxy in attendance. The proposed system is used to detect the face using Adaboost with haar cascades and identification for PCA with LDA and is used to build the real-time face recognition system in multiple faces. the system is used to avoid the manpower to take attendance.

Reference :

    1. Zhao, W., Chellappa, R., Phillips, P. J., Rosenfeld, A., 2003, Face recognition: A literature survey, ACM Computing Surveys (CSUR), V. 35, Issue 4, pp. 399-458
    2. Elham Bagherian, Rahmita Wirza O.K. Rahmat, Facial feature extraction for face recognition: a review, Information Technology, 2008. ITSim 2008. International Symposium, Volume: 2, pp. 1-9
    3. KIRBY, M. AND SIROVICH, L. 1990. “Application of the Karhunen-Loeve procedure for the characterization of human faces”. IEEE Trans. Patt. Anal. Mach. Intell. 12
    4. Elham Bagherian, Rahmita Wirza O.K. Rahmat, “Facial feature extraction for face recognition: a review,” IEEE, 2008.
    5. T. Kanade, “Picture processing by computer complex and recognition of human faces,” technical report, Dept. Information Science, Kyoto Univ., 1973.
    6. I.J. Cox, J. Ghosn, and P.N. Yianios, “FeatureBased face recognition using mixturedistance,” Computer Vision and Pattern Recognition, 1996.
    7. M. Lades, J.C. Vorbruggen, J. Buhmann, J.Lange, C. Von Der Malsburg, R.P. Wurtz, and M. Konen, “Distortion Invariant object recognition in the dynamic link architecture,” IEEE Trans. Computers, vol. 42, pp. 300-311, 1993
    8. Shuicheng Yan, Huan Wang, Jianzhuang Liu, Xiaoou Tang, Huang, T.S. “Misalignment-Robust Face Recognition” Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, IEEE Xplore , march 2010,vol 19, pages 1087 – 1096
    9. L. Sirovich and M. Kirby, “Low-Dimensional procedure for the characterisation of human faces,” J. Optical Soc. of Am., vol. 4, pp. 519- 524, 1987.
    10. Xiaoyang Tan, Triggs. “Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions “ Dept. of Comput. Sci. & Technol., Nanjing Univ. of Aeronaut. & Astronaut. Nanjing, China, IEEE computer science society, February 2010,vol 19,page 1635.
    11. M. Kirby and L. Sirovich, “Application of the Karhunen- Loève procedure for the characterisation of human faces,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, pp. 831-835, Dec.1990.
    12. Yin Zhang, Zhi-Hua Zhou, “Cost-Sensitive Face Recognition “Nat. Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China IEEE, December 2009
    13. L. Zhao and Y.H. Yang, “Theoretical analysis of illumination in pcabased vision systems,” Pattern Recognition, vol. 32, pp. 547-564, 1999.
    14. A. Pentland, B. Moghaddam, and T. Starner, “View-Based and modular eigenspaces for face recognition,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 84-91, 1994.
    15. Yueming Wang, Jianzhuang Liu, Xiaoou Tang “Robust 3D Face Recognition by Local Shape Difference Boosting” Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Hong Kong, China ,IEEE Xplore, January 2010
    16. Belhumeur, V., Hespanda, J., Kiregeman, D., 1997, “Eigenfaces vs. fisherfaces: recognition using class specific liear projection”, IEEE Trans. on PAMI, V. 19, pp. 711-720.
    17. Roger (Ruo-gu) Zhang, Henry Chang, “A Literature Survey of Face Recognition And Reconstruction Techniques,” December 12, 2005.
    18.  Y. Ryu and S. Oh, “Automatic extraction of eye and mouth fields from a face image using eigenfeatures and multiplayer perceptrons,” Pattern Recognition, vol. 34, no. 12,pp. 2459–2466, 2001.
    19. [19]. D. Cristinacce and T. Cootes, “Facial feature detection using adaboost with shape constraints,” in Proc. 14th British Machine Vision Conference, Norwich, UK, Sep.2003, pp. 231–240.
    20. L. Wiskott, J.M. Fellous, N. Kruger, and C. von der Malsburg, “Face recognition by elastic bunch graph matching,” IEEE Trans. Pattern Analysis and Machine Intelligence,vol. 19, no. 7, pp. 775–779, 1997.
    21. K. Toyama, R. Feris, J. Gemmell, and V. Kruger, “Hierarchical wavelet networks forfacial feature localization,” in Proc. IEEE International Conference on Automatic Face and Gesture Recognition, Washington D.C., 2002, pp. 118–123.
    22.  T.F. Cootes, G.J. Edwards, and C.J. Taylor, “Active appearance models,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 681–685, Jun. 2001.
    23. J. Xiao, S. Baker, I. Matthews, and T. Kanade, “Real-time combined 2D+3D active appearance models,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, pp. 535–542.

Recent Article