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Three-dimensional Face Shape by Local Features Prediction
Ruben Garcia-Zurdo
Pages - 1 - 10     |    Revised - 31-12-2014     |    Published - 31-1-2015
Volume - 9   Issue - 1    |    Publication Date - January / February 2015  Table of Contents
Shape from Shading, Three-dimensional Face Shape, Eigenfeatures, Multivariate Regression.
A method is presented to estimate the 3D face shape from a frontal image using a multivariate linear regression model between intensity and depth features: block based discrete cosine transform (DCT), block based principal component analysis (PCA) and modular PCA (eigenfeatures) coefficients. After noting that between-illumination coefficients variances are smaller than between-subjects coefficients variances, we try to correct illumination variations by discarding the first coefficient in the DCT method but not in the PCA methods, as the between-illumination coefficients variances are distributed over many of them. The lowest fractional error in depth prediction is obtained by using a low number of coefficients and a high overlap degree between blocks. Modular PCA produces the best results when the test image is frontally illuminated as in the training phase. DCT and local PCA are more robust across point source horizontal angle and ambient illumination variations.
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1 J. J. Atick, P. A. Griffin and A. N Redlich. "Statistical approach to shape from shading: Reconstruction of three-dimensional face surfaces from single two-dimensional images." Neural Computation, vol. 8, no 6, pp. 1321-1340, 1996.
2 V. Blanz and T. Vetter. "A morphable model for the synthesis of 3D faces." Proceedings of the 26th annual conference on Computer graphics and interactive techniques, 1999, pp. 187-194.
3 M. Castelán, W. A. Smith and E. R. Hancock. "A coupled statistical model for face shape recovery from brightness images." IEEE Transactions on Image Processing, 2007, vol. 16, no 4, pp. 1139-1151.
4 M. Turk and A. Pentland. "Eigenfaces for recognition." Journal of cognitive neuroscience, vol. 3, no 1, pp. 71-86, 1991.
5 R. Gottumukkal, and V. K. Asari. "An improved face recognition technique based on modular PCA approach." Pattern Recognition Letters, vol. 25, no 4, pp. 429-436, 2004.
6 P. Quintiliano, A. N. Santa-Rosa and R. Guadagnin. "Face recognition based on eigenfeatures." Multispectral Image Processing and Pattern Recognition, 2001, pp. 140-145.
7 Z. M. Hafed and M.D. Levine. "Face recognition using the discrete cosine transform." International Journal of Computer Vision, vol. 43, no 3, pp. 167-188, 2001.
8 W. Chen, M. J. Er, and S. Wu. "Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain." IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2006, vol. 36, no2, pp. 458-466.
9 V. P. Vishwakarma, S. Pandey and M. N. Gupta. "Adaptive histogram equalization and logarithm transform with rescaled low frequency DCT coefficients for illumination normalization." International Journal of Recent Trends in Engineering, vol. 1, no 1, pp. 318-322, 2009.
10 A. R. Chadha, P. P. Vaidya, and M. M. Roja. "Face recognition using discrete cosine transform for global and local features." International Conference on Recent Advancements in Electrical, Electronics and Control Engineering, 2011, pp. 502-505.
11 C. Sanderson, M. Saban and Y. Gao. "On local features for GMM based face verification." International Conference on Information Technology and Applications, 2005, vol. 1, pp. 650-655.
12 C. Sanderson, S. Bengio and Y. Gao. "On transforming statistical models for non-frontal face verification." Pattern Recognition, vol. 39, no 2, pp. 288-302, 2006.
13 Y. Wong, C. Sanderson and B.C. Lovell. "Regression based non-frontal face synthesis for improved identity verification." Computer Analysis of Images and Patterns, Springer Berlin Heidelberg, 2009, pp. 116-124.
14 Chai, X., Shan, S., Chen, X., & Gao, W. "Local linear regression for pose invariant face recognition." International Conference on Automatic Face and Gesture Recognition, 2006, pp. 631-636.
15 S. Gupta, K. R. Castleman, M. K. Markey, and A. C. Bovik. "Texas 3D face recognition database." IEEE Southwest Symposium on Image Analysis & Interpretation, 2010, pp. 97-100.
16 R. Basri, and D. W. Jacobs. "Lambertian reflectance and linear subspaces." IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, vol. 25, no 2, pp. 218-233.
17 I. Craw and P. Cameron. "Parameterising images for recognition and reconstruction." British Machine Vision Conference, Springer London, 1991, pp. 367-370.
18 M. Castelán and J. Van Horebeek. "3D face shape approximation from intensities using Partial Least Squares." IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008, pp. 1-8.
Mr. Ruben Garcia-Zurdo
Colegio Universitario "Cardenal Cisneros" - Spain