There was degradation in performance for the super resolution reconstruction between images from databases with actual camera captured frames. This is due to automated face cropping which introduced more than sub pixel level movements to the frames. Even though an alternative method was adopted where face detection is performed in the first frame and the same corner location is used for cropping in following frames, a better solution would be to implement eyes and mouth location detectors and use these points to generate crop limits.
Lastly, in order to make the face matching algorithm more robust, it would benefit the system if it were able to handle matching faces with occlusions. While this problem is difficult, many people commonly wear glasses or a baseball cap, and face recognition should be possible under such conditions.
Bibliography
[1] V. Bruce, P.J.B. Hancock and A.M. Burton, “Comparisons between human and computer recognition of faces”, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 408-413, Apr 1998.
[2] T. E. Boult, M.-C. Chiang, and R. J. Micheals, “Super-Resolution via Image Warping,”
Super-Resolution Imaging, Ed. S. Chaudhuri, Boston: Kluwer Academic Publishers, 2002, pp. 131-169.
[3] S. Baker and T. Kanade, “Hallucinating Faces,” Fourth International Conf. Automatic
Face and Gesture Recognition, March 2000.
[4] C. Liu, H.-Y. Shum, and C.-S. Zhang. “A Two-Step Approach to Hallucinating Faces: Global Parametric Model and Local Nonparametric Model,” Proc. IEEE Conf. Computer
Vision and Pattern Recognition, December 2001.
[5] D. Capel and A. Zisserman, “Super-Resolution from Multiple Views Using Learnt Image Models,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, December 2001. [6] B. K. Gunturk, A.U. Batur, Y. Altunbasak, M. H. Hayes III, and R. M. Mersereau,
“Eigenface-based Super-resolution for Face Recognition,” Proc. International Conf. on
Image Processing, 2002.
[7] L. Wiskott, J. M. Fellous, N Kruger, and C. vod der Malsburg, “Face recognition by elastic bunch graph matching,” Tech. Rep. IR-INI 96-08, Institute fur
Neuroinformatik, Ruhr-Universitat Bochum, D-44780 Bochum, Germany, 1996. [8] P. Vandewalle, S. Süsstrunk and M. Vetterli, “A Frequency Domain Approach to
Registration of Aliased Images with Application to Super-Resolution”, EURASIP Journal on Applied Signal Processing (special issue on Super-resolution), Vol. 2006, pp. Article ID 71459, 14 pages, 2006.
[9] D. Keren, S. Peleg and R. Brada, “Image sequence enhancement for super-resolution image sequence enhancement,” in Proceeding IEEE Conference on Computer Vision and Pattern Recognition, pp. 742-746, June 1988.
[10] M. Turk and A. Pentland. “Eigenfaces for recognition”. Journal of Cognitive Neuroscience, vol. 3, pp. 71-86, 1991
[11] B. K. Gunturk, A.U. Batur, Y. Altunbasak, M. H. Hayes III, and R. M. Mersereau, “Eigenface-based Super-resolution for Face Recognition,” Proc. International Conf. on
[12] T. Kanade. “Picture Processing by Computer Complex and Recognition of Human Faces.” PhD thesis, Kyoto University, 1973.
[13] CV Reference Manual, “Pattern Recognition: Object Detection”, http://opencvlibrary.sourceforge.net/CvReference#cv_pattern
[14] Paul Viola and Michael Jones, “Rapid Object Detection using a Boosted Cascade of SimpleFeatures”, in Conference on Computer Vision and Pattern Recognition, 2001. [15] R. Lienhart and J. Maydt, “An extended Set of Haar-like Features for Rapid Object
Detection”, IEEE ICIP 2002, vol 1, pp. 900-903, Sep.2002.
[16] V. Bruce, P.J.B. Hancock and A.M. Burton, “Comparisons between human and computer recognition of faces”, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 408-413, Apr 1998.
[17] G. Yongsheng and M.K.H. Leung, “Face recognition using line edge map”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, Iss.6, pp.764- 779, Jun 2002
[18] J.E. Meng, W. Chen and W. Shiqian, “Highspeed face recognition based on discrete cosine transform and RBF neural networks”; IEEE Transactions on Neural Networks vol. 16, Issue 3, pp.679 – 691, May 2005.
[19] S. Da-Rui and W. Le-Nan, “A local-to-holistic face recognition approach using elastic graph matching” Proc. International Conference on Machine Learning and Cybernetics, vol. 1, pp. 240 - 242, 4-5 Nov. 2002.
[20] W. Haiyuan, Y. Yoshida, T. Shioyama, “Optimal Gabor filters for high speed face identification” Proc. 16th International Conference on Pattern Recognition, vol. 1, pp. 107 - 110, Aug 2002.
[21] R.Y. Tsai and T. S. Huang, “Multiframe image restoration and registration”, in Advance in Computer Vision and Image Processing. JAI Press, 1984, vol. 1, pp. 317- 339.
[22] S. Park, M. Park, and M. Kang, “Super resolution image reconstruction: a technical overview,” IEEE Signal Processing Magazine, pp. 21-36, May 2003.
[23] S. Borman and R. Stevenson, “Spatial resolution enhancement of low-resolution image sequences – a comprehensive review with direction for future research,” University of Notre Dame, Tech Rep., 1998. [Online]. Availabe:
http://www.nd.edu/~sborman/publications/SReview.pdf
[24] S. Park, M. Park, and M. Kang, “Super resolution image reconstruction: a technical overview,” IEEE Signal Processing Magazine, pp. 21-36, May 2003.
[26] S. Kim, N. Bose, and H. Valenzuela, “Recursive reconstruction of high resolution image from noisy under sampled multi-frames”, IEEE Trans. Acous., Speech, Signal Processing, vol. 38, pp. 1013-1027, June 1990.
[27] B. Marcel, M. Briot and R. Murrieta, “Calcul de translation et rotation par la transformation de Fourier.” Traitement du Signal, vol. 14, no. 2, pp 135-149, 1997. [28] M. Irani and S. Peleg, “Improve resolution by image registration,” Graph. Models
Image Process., vol. 53, pp.231-239, March 1991.
[29] R. Schulz and R. Stevenson, “Extraction of high-resolution frames from video sequences,” IEEE Trans. Image Procesing., vol. 5, pp. 996-1011, June 1996. [30] R. Hardie, K. Barnard, and E. Armstrong, “Joint MAP registration and high-
resolution image estimation using a sequence of under sampled images,” IEEE Trans. Image Procesing., vol. 6, pp. 1621-1633, Dec. 1997.
[31] J. R. Bergen, P. Anandan, K. J. Hanna, and R. Hingorani, “Hierarchical model-based motion estimation,” in Proceedings of 2nd European Conference on Computer Vision (ECCV ’92), Lecture Notes in Computer Science, pp. 237–252, Santa Margherita Ligure, Italy, May 1992.
[32] F. Lin, J.Cook, V. Chandran and S. Sridharan, “ Face recognition from super resolved images” IEEE Trans. Image Procesing, Volume 12, Issue 5, pp. 597-606, May 2003.
[33] Osman Gokhan Sezer, Yucel Altunabasak and Aytul Ercil, “Face recognition with independent component based super-resolution,” IEEE Trans. Neural Networks, Volume 13, Issue 6, pp. 1450-1464, Nov 2002.
[34] B. S. Reddy and B. N. Chatterji, “An FFT-based technique for translation, rotation, and scale-invariant image registration,” IEEE Transactions on Image Processing, vol. 5, no. 8, pp. 1266–1271, 1996.
[35] R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison Wesley, 2nd Ed. 2002
[36] C. Liu, H. Wechsler, “Comparative Assessment of Independent Component Analysis (ICA) for Face Recognition,” Proc. of the Second International Conference on Audio- and Video-based Biometric Person Authentication, AVBPA'99, pp. 211-216, Washington D.C., USA, March 1999.
[37] M. Bartlett and T. Sejnowski. “Independent components of face images: A
representation for face recognition,” Proc. the 4th Annual Joint Symposium on Neural Computation, Pasadena, CA, May 17, 1997.
[38] L. Wiskott, J.-M. Fellous, N. Krueuger, C. von der Malsburg, “Face Recognition by Elastic Bunch Graph Matching,” Chapter 11 in Intelligent Biometric Techniques in Fingerprint and Face Recognition, Eds. L.C. Jain et al., CRC Press, pp. 355-396, 1999.
[39] B. Moghaddam, T. Jebara, A. Pentland, “Bayesian Face Recognition, Pattern Recognition,” Vol. 33, Issue 11, pp. 1771-1782, November 2000.
[40] C. Liu, H. Wechsler, “A Unified Bayesian Framework for Face Recognition,” Proc. of the 1998 IEEE International Conference on Image Processing, pp. 151-155, October 1998.
[41] B. Moghaddam, T. Jebara, A. Pentland, “Bayesian Face Recognition,” Pattern Recognition, Vol. 33, Issue 11, pp. 1771-1782, November 2000.
[42] C. Liu, H. Wechsler, “A Unified Bayesian Framework for Face Recognition,” Proc. of the 1998 IEEE International Conference on Image Processing, pp. 151-155, October 1998.
[43] P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection,” Proc. of the 4th European Conference on Computer Vision, ECCV'96, Cambridge, UK, pp. 45-58, April 1996. [44] Vidit Jain, Amitabha Mukherjee. The Indian Face Database. http://vis-
www.cs.umass.edu/~vidit/IndianFaceDatabase/ , 2002.
[45] Peter Peer, CVL Face Database: http://www.lrv.fri.uni-lj.si/facedb.html [46] VPA Superresolution Face Database:
https://vpa.sabanciuniv.edu/phpBB2/viewtopic.php?t=4284&sid=9a62c4dd1e90800ef c344e31f3535faa
[47] Open Source Computer Vision Library,
http://www.intel.com/technology/computing/opencv/
[48] M. Turk, A. Pentland. “Eigenfaces for Recognition”. Journal of Cognitive Neuroscience. Vol 3, No. 1, pp. 71-86, 1991
[49] Logitech Orbit Spehere AF Specs
http://www.logitech.com/index.cfm/webcam_communications/webcams/devices/348 0&cl=au,en