Abstract
In this paper, we propose an innovative face hallucination approach based on principle component analysis (PCA) and residue technique. First, the relationship of projection coefficients between high-resolution and low-resolution images using PCA is investigated. Then based on this analysis, a high resolution global face image is constructed from a low resolution one. Next a high-resolution residue is derived based on the similarity between the projections on high and low resolution residue training sets. Finally by combining the global face and residue in high resolution, a high resolution face image is generated. Also the recursive and two-stage methods are proposed, which improve the results of face image enhancement. Extensive experiments validate the proposed approaches.
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References
Pratt WK (1991) Digital image processing. Wiley-Interscience, New York
Wolberg G (1992) Digital image warping. IEEE Computer Society Press, Los Alamitos
Chen TC, de Figueiredo RJP (1985) Two-dimensional interpolation by generalized spline filters based on partial differential equation image models. IEEE Trans Acoust Speech Signal Process 33(3):631–642
Karayiannis NB, Venetsanopolous AN (1991) Image interpolation based on variational principles. Signal Process 25(3):259–288. ISSN 0165-1684, doi:10.1016/0165-1684(91)90114-X
Xue K, Winans A, Walowit E (1992) An edge-restricted spatial interpolation algorithm. J Electr Imaging 01(02):152–161
Schultz R, Stevenson R (1994) A Bayesian approach to image expansion for improved definition. IEEE Trans Image Process 3(3):233–242
Freeman WT, Pasztor EC (1999) Learning low-level vision. In: Proceedings of ICCV ’99. Kerkyra, Greece, pp 1182–1189
Hertzmann A, Jacobs CE, Oliver N, Curless B, Salesin DH (2001) Image analogies. In: Proceedings of SIGGRAPH ’01. Los Angeles, California, pp 327–340
Tang Y, Yan P, Yuan Y, Li X (2011) Single-image super-resolution via local learning. Int J Mach Learn Cybern 2(1):15–23
Baker S, Kanade T (2000) Hallucinating faces. In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition. Grenoble, France, pp 83–88
Liu C, Shum HY, Freeman WT (2007) Face hallucination: theory and practice. IJCV 75(1):115–134
Liu C, Shum HY, Zhang CS (2001) A two-step approach to hallucinating faces: global parametric model and local nonparametric model. In: Proceedings of CVPR’01. Kauai Marriott, Hawaii, pp 192–198
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3:71–86
Swets DL, Weng JJ (1996) Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell 18(8):831–836
Poon B, Ashraful Amin M, Yan H(2011) Performance evaluation and comparison of PCA based human face recognition methods for distorted images. Int J Mach Learn Cybern. doi:10.1007/s13042-011-0023-2
Silverstein JW (1986) Eigenvalues and eigenvectors of large-dimensional sample covariance matrices. Contemp Math 50:153–159
Smith LI (2002) A tutorial on principal components analysis, 26 Feb 2002
Shlens J (2005) A tutorial on principal component analysis, Version 2, 10 Dec 2005
Wang XG, Tang XO (2005) Hallucinating face by Eigentransformation. IEEE Trans Syst Man Cybern 35(3):425–434
Zhuang Y, Zhang J, Wu F (2007) Hallucinating faces: LPH super-resolution and neighbor reconstruction for residue compensation. Pattern Recognit 40(11):3178–3194
Yang J, Wright J, Huang T, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process (TIP) 19(11):2861–2873
Jia K, Gong S (2008) Generalized face super-resolution. IEEE Trans Image Process 17(6):873–886
Huang H, He H, Fan X, Zhang J (2010) Super-resolution of human face image using canonical correlation analysis. Pattern Recognit 43(7):2532–2543
Liang Y, Lai J-H, Xie X, Liu W (2010) Face hallucination under an image decomposition perspective. In: International Conference on Pattern Recognition, Istanbul
Zhang W, Cham W-K (2008) Learning-based face hallucination in DCT domain. In: IEEE Conference on Computer Vision and Pattern Recognition
Yang J, Wright J, Huang T, Ma Y(2008) Image super-resolution as sparse representation of raw image patches. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Baker S, Kanade T (2002) Limits on super-resolution and how to break them. IEEE Trans Patern Anal Mach Intell 24(9):1167–1183
Penve PS, Sirovich L (2000) The global dimensionality of face space. In: IEEE International Conference on Automatic Face and Gesture Recognition
Kimmel R (1999) Demosaicing: image reconstruction from color CCD samples. IEEE Trans Image Process 8(9):1221–1228
Lee C, Eden M, Unser M (1998) High-quality image resizing using oblique projection operators. IEEE Trans Image Process 7(4):679–692
Li X, Orchard MT (2001) New edge-directed interpolation. IEEE Trans Image Process 10(10):1521–1527
Borman S, Stevenson RL (1998) Spatial resolution enhancement of low-resolution image sequences, A comprehensive review with directions for future research, Lab. Image and Signal Analysis, University of Notre Dame, Tech. Rep
Borman S, Stevenson RL (1999) Super-resolution from image sequences—a review. In: Proc.1998 Midwest Symp. Circuits and systems
Shah MR, Zakhor A (1999) Resolution enhancement of color video sequences. IEEE Trans Image Process 8(6):879–885
Rhee SH, Kang MG (1999) Discrete cosine transform based regularized high-resolution image reconstruction algorithm. Opt Eng 38(8):1348–1356
Nguyen N, Milanfar P (2000) An efficient wavelet-based algorithm for image superresolution. In: Proc. Int. Conf. Image Processing, pp 351–354
Zhao W, Chellapa R, Philips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458
He XF, Niyogi P (2003) Locality preserving projections. In: Neural Information Processing Systems 16. Vancouver, Canada
Candes EJ, Wakin MB (2008) An introduction to compressive sampling. IEEE Signal Process Mag 25(2):21–30
Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces-recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720
Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am 14(8):1724–1733
Moghaddam B, Wahid W, Pentland A (1998) Beyond eigenfaces: Probabilistic matching for face recognition. In: IEEE Int’l Conf on Automatic Face and Gesture Recognition, pp 30–35
Chang Y, Hu CB, Turk M (2004) Probabilistic expression analysis on manifolds. In: CVPR, pp 520–527
Belkin M, Niyogi P (2002) Laplacian Eigenmaps and spectral techniques for embedding and clustering. In: Neural Information Processing Systems, pp 585–591
Phillips PJ, Rauss P, Der S (1996) FERET(face-recognition technology) recognition algorithm development and test report, Technical Report 995, U.S. Army Research Laboratory
Gong X, Li X-X, Feng L, Xia R (2011) A robust framework for face contour detection from clutter background. Int J Mach Learn Cybern. doi:10.1007/s13042-011-0044-x
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Xu, X., Liu, W. & Venkatesh, S. An innovative face image enhancement based on principle component analysis. Int. J. Mach. Learn. & Cyber. 3, 259–267 (2012). https://doi.org/10.1007/s13042-011-0060-x
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DOI: https://doi.org/10.1007/s13042-011-0060-x