Tharshini Gunendradasan, Chinthaka Dinesh, Roshan I. Godaliyadda, and Mervyn P.B. Ekanayake


  1. [1] M.T. Khan, F. Nasir, M.U. Qadir, and C.W. de Silva, Multi-robot cooperation framework based on artificial immune system, Journal of Control and Intelligent Systems, 43(3), 2015.
  2. [2] W. Deng, H. Zhao, Y. Luo, and X. Li. A mixed optimization method based on artificial intelligence and its application, Journal of Control and Intelligent Systems, 43(4), 2015.
  3. [3] R. Miragaia, M.A. Vega-Rodr´ıguez, J.A. G´omez-Pulido, and J.M. S´anchez-P´erez, A biometric security system based on a hybrid face recognition technique. Proceedings of the 10th ACTA International Conference on Computer Graphics and Imaging, Anaheim, CA, USA, 2008, 106–111.
  4. [4] N. Zaeri, F. Mokhtarian, and A. Cherri, Efficient face recognition for wireless surveillance systems, Proceedings of the 9th ACTA Conference on Computer Graphics and Imaging, Anaheim, CA, USA, 2008, 132–137.
  5. [5] H. Mohammadzade and D. Hatzinakos, Projection into expression subspaces for face recognition from single sample per person, IEEE Transactions on Affective Computing, 4(1), 2013, 69–82.
  6. [6] A.S. Mian, M. Bennamoun, and R. Owens, An efficient mul-timodal 2D-3D hybrid approach to automatic face recogni-tion. IEEE Transactions on Pattern Analysis and MachineIntelligence, 29(11), 2007, 1927–1943.
  7. [7] Q. Liu and C. Liu, A new locally linear KNN method withan improved marginal fisher analysis for image classification, Proceedings of the IEEE Joint Conference on Biometrics, San Francisco, CA, 2014, 1–6.
  8. [8] K.C. Kwak and W. Pedrycz, Face recognition using an enhanced independent component analysis approach, IEEE Transactions on Neural Networks, 18(2), 2007, 530–541.
  9. [9] M.S.U. Sarwar, A. Sharin, M.R. Khan, H. Imtiaz, and S.A. Fattah, A face recognition scheme based on spatial correlation function, Proceedings of the 10th IEEE Conference on Technologies for Smart Nation, 2010, 671–674.
  10. [10] ¨U.C¸. Turhal, A. Duysak, and M.B. G¨ulmezo˘glu, A two stage algorithm for face recognition: 2DPCA and within-class scatter minimization, Proceedings of the 4th ACTA International Conference on Signal Processing, Pattern Recognition, and Applications, Anaheim, CA, USA, 2007, 338–342.
  11. [11] X. Li, G. Mori, and H. Zhang, Expression-invariant face recognition with expression classification, Proceedings of the 3rd IEEE Conference on Computer and Robot Vision, 2006, 77–77.
  12. [12] J. Wang, K.N. Plataniotis, J. Lu, and A.N. Venetsanopoulos, On solving the face recognition problem with one training sample per subject. Journal of Pattern recognition, 39(9), 2006, 1746–1762.
  13. [13] G. Tharshini, H.G.C.P. Dinesh, G.M.R.I. Godaliyadda and M.P.B. Ekanayake, A robust expression negation algorithm for accurate face recognition for limited training data, Proceedings of the 10th IEEE Conference on Industrial and Information systems, Peradeniya, 2015, 384–389.
  14. [14] Y. Rahulamathavan, R.C.W. Phan, J.A. Chambers and D.J.Parish, Facial expression recognition in the encrypted domain based on local fisher discriminant analysis, IEEE Transactions on Affective Computing, 4(1), 2013, 83–92.
  15. [15] R. Zhi and Q. Ruan, Discriminant spectral analysis for facial expression recognition, Proceedings of the IEEE Conference on Image Processing (ICIP), 2010, 1056–1059.
  16. [16] P. Ekman and W. Friesen, Facial action coding system: a technique for the measurement of facial movement, Consulting Psychologists, San Francisco, 1978.
  17. [17] S. Wold, K. Esbensen, and P. Geladi, Principal component analysis, Chemometrics and Intelligent Laboratory Systems, 2(1–3), 1987, 37–52.
  18. [18] P. Lucey, J.F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, The extended Cohn–Kanade dataset (ck+):A complete dataset for action unit and emotion-specifiedexpression, Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition, San Francisco, CA, 2010,94–101.

Important Links:

Go Back