Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(551.89KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Face Hallucination using Eigen Transformation in Transform Domain
Abdu Rahiman V, Jiji Victor Charangatt
Pages - 265 - 282     |    Revised - 30-09-2009     |    Published - 20-02-2010
Volume - 3   Issue - 6    |    Publication Date - January 2010  Table of Contents
MORE INFORMATION
KEYWORDS
Face hallucination, Eigen transformation, Wavelets, Discrete cosine transform
ABSTRACT
Faces often appear very small in surveillance imagery because of the wide fields of view that are typically used and the relatively large distance between the cameras and the scene. In applications like face recognition, face detection etc. resolution enhancement techniques are therefore generally essential. Super resolution is the process of determining and adding missing high frequency information in the image to improve the resolution. It is highly useful in the areas of recognition, identification, compression, etc. Face hallucination is a subset of super resolution. This work is intended to enhance the visual quality and resolution of a facial image. It focuses on the eigen transform based face super resolution techniques in transform domain. Advantage of eigen transformation based technique is that, it does not require iterative optimization techniques and hence comparatively faster. Eigen transform is performed in wavelet transform and discrete cosine transform domains and the results are presented. The results establish the fact that the eigen transform is efficient in transform domain also and thus it can be directly applied with slight modifications on the compressed images.
CITED BY (5)  
1 George, J. P. (2012). development of efficient biometric recognition algorithms based on fingerprint and face (Doctoral dissertation, Christ University, Bangalore).
2 George, J., Tevaramani, S. S., & Raja, K. B. (2012). Performance comparison of face recognition using transform domain techniques. World of Computer Science and Information Technology Journal (WCSIT), 2(3), 82-89.
3 Kekre, H. B., Sarode, T. K., & Tirodkar, A. A. (2011). A Comprehensive Comparison of the Performance of Fractional Coefficients of Image Transforms for Palm Print Recognition. International Journal of Computer Science and Information Security, 9(10), 84.
4 M. K. Bhowmik, D. Bhattacharjee, M. Nasipuri, D. K. Basu, M. Kundu."Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human Face Recognition – A Comparative Study". International Journal of Image Processing (IJIP), 4(1):1-12
5 Bhowmik, M. K., Bhattacharjee, D., Nasipuri, M., Basu, D. K., & Kundu, M. (2010). Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human Face Recognition-A Comparative Study. arXiv preprint arXiv:1007.0626.
1 Directory of Open Access Journals (DOAJ)
2 Google Scholar
3 ScientificCommons
4 Academic Index
5 CiteSeerX
6 refSeek
7 iSEEK
8 Socol@r
9 ResearchGATE
10 Bielefeld Academic Search Engine (BASE)
11 OpenJ-Gate
12 Scribd
13 WorldCat
14 slideshare
15 PDFCAST
16 PdfSR
1 Simon Baker and Takeo Kanade, ’Hallucinating Faces’, In Fourth International Conference on Automatic Face and Gesture Recognition, 2000.
2 Simon Baker and Takeo Kanade, ’Limits on Super resolution and how to break them’, In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002.
3 Ce Liu, Heung-Yeung Shum and Chang Shui Zhang, ’A Two Step Approach to Hallucinating Faces: Global Parametric Model and Local Parametric Model’, In Proc. of IEEE International Conference on Computer Vision and Pattern Recognition, 2001.
4 Ce Liu, Heung-Yeung Shum and William T. Freeman, ’Face Hallucination: Theory and Practice’, In International Journal of Computer vision Springer, 2007.
5 I.Daubechies, ’Ten Lectures on Wavelets’, SIAM, Philadelphia, 1992.
6 David Capel and Andrew Zisserman, Super-resolution from multiple views using learnt image models. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), 2001.
7 A. Hossen and U. Heute, ’2D Subband Transforms: Theory and Applications’, In IEE Proc. Vis. Image Signal Processing, Vol. 151, No. 5, October, 2004
8 Jiji C.V., M.V. Joshi and Subhasis Chaudhuri, ’Single frame Image Super-resolution Using Learned Wavelet Coefficients’, In International Journal of Imaging Systems and Technology, 2004.
9 J. Makhoul, ’A Fast Cosine Transform in One and Two Dimensions’, In IEEE Tran. Acoustic and Speech Signal Processing, 28(1), 1980
10 Todd K. Moon and Wynn C. Stirling, ’Mathematical Methods and Algorithms for Signal Processing’, Pearson Education, 2005.
11 M.Turk and A. Pentland, ’Eigenface for Recognition’, In Journal of Cognitive Newroscience,1991.
12 Gonzalez and Woods, ’Digital Image Processing’, Prentice Hall India.
13 X. Wang and X. Tang, ’Face Hallucination and Recognition’, In Proc. 4th Int. Conf. Audio and video based Personal Authentication, IAPR, University of Surrey, Guildford, UK, 2003.
14 X. Wang and X. Tang, ’Hallucinating Faces by Eigen transformation’, In IEEE Transactions on systems, man and cybernetics- Part C: Applications and Reviews, 2005.
15 Wayo Puyati, Somsak Walairacht and Aranya Walairacht, ’PCA in wavelet domain for face recognition’, Department of computer Engineering, King Mongkut’s Institute of technology, Bankok, ICACT 06, 2006.
16 M. Choi, R. Y. Kim, M. R. Nam, and H. O. Kim., “Fusion of Multispectral and Panchromatic Satellite Images Using the Curvelet Transform”, IEEE Transactions on Goescience and Remote Sensing, 2(2):136–140, 2005.
Mr. Abdu Rahiman V
- India
vkarahim@gmail.com
Dr. Jiji Victor Charangatt
College of Engineering - India