Deep S. Dev and Dakshina R. Kisku


  1. [1] J. Lewis, Fast template matching, Proc. Vision Interface, Quebec City, Canada, 1995, 120–123.
  2. [2] T. Mahalakshmi, R. Muthaiah, and P. Swaminathan, An overview of template matching technique in image processing, Research Journal of Applied Sciences, Engineering and Technology, 4(24), 2012, 5469–5473.
  3. [3] W. Ouyang, R. Zhang, and W.K. Cham, Fast pattern matching using orthogonal Haar transform, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, 2010, 3050–3057.
  4. [4] P.F. Felzenszwalb, R.B Girshick, D. McAllester, and D. Ramanan, Object detection with discriminatively trained part-based models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 2010, 1627–1645.
  5. [5] Y. Li, H. Li, and Z. Cai, Fast orthogonal Haar transform pattern matching via image square sum, IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(9), 2014, 1748–1760.
  6. [6] M.R. Ayoob and R.M.S. Kumar, Face recognition using symmetric local graph structure, Indian Journal of Science and Technology, 8(24), 2015, DOI: 10.17485/ijst/2015/v8i24/80876.
  7. [7] L.D. Stefano and S. Mattoccia, Fast template matching using bounded partial correlation, Machine Vision and Applications, 13, 2003, 213–221.
  8. [8] Y.M. Fouda, A robust template matching algorithm based on reducing dimensions, Journal of Signal and Information Processing, 6, 2015, 109–122.
  9. [9] D.G. Lowe, Distinctive image features from scale-invariant key points, International Journal of Computer Vision, 60(2), 2004, 91–110.
  10. [10] E.E.A. Abusham and H.K. Bashir, Face recognition using local graph structure (LGS), Lecture Notes in Computer Science, 6762, 2011, 169–175.
  11. [11] C. Lampert, M. Blaschko, and T. Hofmann, Beyond sliding windows: Object localization by efficient subwindow, Computer Vision and Pattern Recognition, Anchorage, Alaska, USA, 2008.
  12. [12] W. Ouyang and W.K. Cham, Fast algorithm for Walsh Hadamard transform on sliding windows, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(1), 2010, 165–171.
  13. [13] F. Tombari, S. Mattoccia, and L.D. Stefano, Full search-equivalent pattern matching with incremental dissimilarity approximations, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(1), 2009, 129–141.
  14. [14] K. Ahuja and P. Tuli, Object recognition by template matching using correlations and phase angle method, International Journal of Advanced Research in Computer and Communication Engineering, 2(3), 2013, 1768–1773.
  15. [15] S. Korman, D. Reichman, G. Tsur, and S. Avidan, FAsT match: Fast affine template matching, Computer Vision and Pattern Recognition, Portland, 2013.
  16. [16] M. Turk and A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience, 3(1), 1991, 71–86.
  17. [17] K. Etemad and R. Chellappa, Discriminant analysis for recognition of human face images, Journal of the Optical Society of America, 14, 1997, 1724–1733.
  18. [18] J. Yang, D. Zhang, A.F. Frangi, and J. Yang, Two-dimensional PCA: A new approach to appearance-based face representation and recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(1), 2004, 131–137.
  19. [19] B. Pentland, B. Moghaddam, and T. Starner, View-based and modular eigenspaces for face recognition, Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, Seattle, Washington, 1994, 84–91.
  20. [20] A.R. Chadha, P.P. Vaidya, and M.M. Roja, Face recognition using discrete cosine transform for global and local features, 2011, Sivakasi, Tamilnadu, India. IEEE Xplore: CFP1153R-ART; ISBN: 978-1-4577-2149-6.
  21. [21] S. Muhammad, A. Khalid, M. Raza, and S. Mohsin, Face recognition using gabor filters, Journal of Applied Computational Science and Mathematics, 11(5), 2011, 53–57.
  22. [22] G. Bai, Y. Zhu, and Z. Ding, A hierarchical face recognition method based on local binary pattern, Congress on Image and Signal Proc., IEEE, Sanya, Hainan, 2008, 610–614. Available from:
  23. [23] M.A. Rahim, M.N. Hossain, T. Wahid, and M.S. Azam, Face recognition using local binary patterns (LBP), Global Journal of Computer Science and Technology Graphics & Vision, 13(4), 2013, Version 1.0, ISSN: 0975-4172 & Print ISSN: 0975-4350.
  24. [24] B. Froba and A. Ernst, Face detection with the modified census transform, Proc. of the 6th IEEE International Conf. on Automatic Face and Gesture Recognition, Seoul, South Korea, 2004.
  25. [25] L. Zhang, R. Chu, S. Xiang, S. Liao, and S.Z. Li, Face detection based on multi-block LBP representation, Proc. of the IAPR/IEEE International Conf. on Biometrics, Seoul, Korea, 2007.
  26. [26] H. Zhang, W. Gao, X. Chen, and D. Zhao, Object detection using spatial histogram features, Image and Vision Computing, 24(4), 2006, 327–341.
  27. [27] S. Yan, S. Shan, X. Chen, and W. Gao, Locally assembled binary (LAB) feature with feature-centric cascade for fast and accurate face detection, Proc. of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, Anchorage, Alaska, USA, 2008.
  28. [28] Y. Lin, T. Liu, and C. Fuh, Fast object detection with occlusions, Proc. of the European Conf. on Computer Vision, Prague, Czech Republic, 2004, 402–413.
  29. [29] L. Goldmann, U. Monich, and T. Sikora, Components and their topology for robust face detection in the presence of partial occlusions, IEEE Transactions on Information Forensics and Security, 2(3), 2007, 559–569. 303
  30. [30] P. Sinha, Qualitative representations for recognition, Biologically Motivated Computer Vision Workshop, Tübingen, Germany, 2002.
  31. [31] J. Sadr, S. Mukherjee, K. Thoresz, and P. Sinha, Toward the fidelity of local ordinal encoding, Proc. of the Annual Conf. on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2001.
  32. [32] J. Shotton, M. Johnson, and R. Cipolla, Semantic texton forests for image categorization and segmentation, IEEE Conf. on Computer Vision and Pattern Recognition, Anchorage, Alaska, June 2008.
  33. [33] K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), 2005, 1615–1630.
  34. [34] Y. Kobayashi, T. Okamoto, and M. Onishi Generation of obstacle avoidance based on image features and embodiment, International Journal of Robotics and Automation, 24(4), 2012, 364–376.
  35. [35] D. Ren, J. Chen, C. Zhang, Z. Liu, X. Liu, and H. Zhou, An adaptive illumination pre-processing method for face recognition, International Journal of Robotics and Automation, 32(5), 2017, 509–516.
  36. [36] J. Chen and B. Tiddeman, Multi-cue facial feature detection and tracking under various illuminations, International Journal of Robotics & Automation, 25(2), 2010, 162–171.
  37. [37] S.A. Nene, S.K. Nayar, and H. Murase, Columbia object image library (COIL-100), Technical Report CUCS-006-96, 1996.
  38. [38] G. Wang, Y. Zhang, and L. Fei-Fei, Using dependant regions or object categorization in a generative framework, IEEE Conf. on Computer Vision and Pattern Recognition, New York, NY, USA, 2006.

Important Links:

Go Back