Hyperspectral Face Recognition with Patch-Based Low Rank Tensor Decomposition and PFFT Algorithm
Abstract
:1. Introduction
2. Related Work
3. Extraction Features for Hyperspectral Facial Image
3.1. Local Feature Extraction
3.1.1. Discriminative Patch Selection
3.1.2. Discriminant Local Feature Extraction
3.2. Global Feature Extraction
4. Ensemble Classification for Hyperspectral Facial Images
4.1. Construction of Ensemble Classifier
4.2. Combination of Multiple Classifiers
5. Results and Discussion
5.1. Hyperspectral Face Database
5.2. Experiments on Global and Local Classifiers
5.2.1. Experiments on the Global Classifier
5.2.2. Experiments on the Local Classifier
Rank Parameters Learning and Patch Selection
Local Patch Classifiers and Their Ensemble
5.3. Experiments on the Ensemble Classifier
5.3.1. Comparison of Different Ensemble Methods
5.3.2. Comparison of Different Classification Methods
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Lu, G.; Fei, B. Medical hyperspectral imaging: A review. J. Biomed. Opt. 2014, 19, 10901. [Google Scholar] [CrossRef] [PubMed]
- Zonios, G.; Bykowski, J.; Kollias, N. Skin Melanin, Hemoglobin, and Light Scattering Properties Can Be Quantitatively Assessed In Vivo Using Diffuse Reflectance Spectroscopy. J. Investig. Dermatol. 2001, 117, 1452–1457. [Google Scholar] [CrossRef] [PubMed]
- Pan, Z.; Healey, G.E.; Prasad, M.; Tromberg, B.J. Face recognition in hyperspectral images. IEEE Trans. Pattern Anal. Mach. Intell. 2003, 25, 1552–1560. [Google Scholar] [Green Version]
- Ryer, D.M.; Bihl, T.J.; Bauer, K.W.; Rogers, S.K. QUEST hierarchy for hyperspectral face recognition. Adv. Artif. Intell. 2012, 2012, 1. [Google Scholar] [CrossRef]
- Uzair, M.; Mahmood, A.; Shafait, F.; Nansen, C.; Mian, A. Is spectral reflectance of the face a reliable biometric? Opt. Express 2015, 23, 15160–15173. [Google Scholar] [CrossRef] [PubMed]
- Di, W.; Zhang, L.; Zhang, D.; Pan, Q. Studies on Hyperspectral Face Recognition in Visible Spectrum with Feature Band Selection. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 2010, 40, 1354–1361. [Google Scholar] [CrossRef]
- Uzair, M.; Mahmood, A.; Mian, A. Hyperspectral Face Recognition using 3D-DCT and Partial Least Squares. In Proceedings of the BMVC, Bristol, UK, 9–13 September 2013. [Google Scholar]
- Uzair, M.; Mahmood, A.; Mian, A. Hyperspectral Face Recognition with Spatiospectral Information Fusion and PLS Regression. Image Process. IEEE Trans. 2015, 24, 1127–1137. [Google Scholar] [CrossRef]
- Shen, L.; Zheng, S. Hyperspectral face recognition using 3D Gabor wavelets. In Proceedings of the International Conference on Pattern Recognition, Tsukuba, Japan, 11–15 November 2012; pp. 1574–1577. [Google Scholar]
- Zhu, Z.; Jia, S.; He, S.; Sun, Y.; Ji, Z.; Shen, L. Three-dimensional Gabor feature extraction for hyperspectral imagery classification using a memetic framework. Inf. Sci. 2015, 298, 274–287. [Google Scholar] [CrossRef]
- Pan, Z.; Healey, G.; Tromberg, B. Comparison of Spectral-Only and Spectral/Spatial Face Recognition for Personal Identity Verification. Eurasip J. Adv. Signal Process. 2009, 2009, 8. [Google Scholar] [CrossRef]
- Turk, M.A.; Pentland, A.P. Face recognition using eigenfaces. J. Cogn. Neurosci. 2012, 3, 71–86. [Google Scholar] [CrossRef]
- Mairal, J.; Elad, M.; Sapiro, G. Sparse Representation for Color Image Restoration. IEEE Trans. Image Process. 2007, 17, 53–69. [Google Scholar] [CrossRef]
- Wright, J.; Ma, Y.; Mairal, J.; Sapiro, G.; Huang, T.S.; Yan, S. Sparse Representation for Computer Vision and Pattern Recognition. Proc. IEEE 2010, 98, 1031–1044. [Google Scholar] [CrossRef] [Green Version]
- Yang, M.; Zhang, L.; Yang, J.; Zhang, D. Robust sparse coding for face recognition. In Proceedings of the Computer Vision and Pattern Recognition, Colorado Springs, CO, USA, 20–25 June 2011; pp. 625–632. [Google Scholar]
- Vasilescu, M.A.O.; Terzopoulos, D. Multilinear Analysis of Image Ensembles: TensorFaces. In Proceedings of the European Conference on Computer Vision, Copenhagen, Denmark, 28–31 May 2002; pp. 447–460. [Google Scholar]
- Savas, B.; Eldén, L. Handwritten digit classification using higher order singular value decomposition. Pattern Recognit. 2007, 40, 993–1003. [Google Scholar] [CrossRef]
- Li, C.; Ma, Y.; Huang, J.; Mei, X.; Ma, J. Hyperspectral image denoising using the robust low-rank tensor recovery. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 2015, 32, 1604–1612. [Google Scholar] [CrossRef] [PubMed]
- Lai, J.H.; Yuen, P.C.; Feng, G.C. Face recognition using holistic Fourier invariant features. Pattern Recognit. 2001, 34, 95–109. [Google Scholar] [CrossRef]
- Sao, A.K.; Yegnanarayana, B. On the use of phase of the Fourier transform for face recognition under variations in illumination. Signal Image Video Process. 2010, 4, 353–358. [Google Scholar] [CrossRef]
- Jing, X.Y.; Wong, H.S.; Zhang, D. Face recognition based on discriminant fractional Fourier feature extraction. Pattern Recognit. Lett. 2006, 27, 1465–1471. [Google Scholar] [CrossRef]
- Zhang, D.; Ding, D.; Li, J.; Liu, Q. A novel way to improve facial expression recognition by applying Fast Fourier Transform. In Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong, China, 12–14 March 2014. [Google Scholar]
- Angadi, S.A.; Kagawade, V.C. A Robust Face Recognition Approach through Symbolic Modeling of Polar FFT features. Pattern Recognit. 2017, 71, 235–248. [Google Scholar] [CrossRef]
- Zhao, R.; Li, X.; Sun, P. An improved windowed Fourier transform filter algorithm. Opt. Laser Technol. 2015, 74, 103–107. [Google Scholar] [CrossRef]
- Yan, Y.; Wang, H.; Suter, D. Multi-subregion based correlation filter bank for robust face recognition. Pattern Recognit. 2014, 47, 3487–3501. [Google Scholar] [CrossRef] [Green Version]
- Li, H.; Shen, F.; Shen, C.; Yang, Y.; Gao, Y. Face recognition using linear representation ensembles. Pattern Recognit. 2016, 59, 72–87. [Google Scholar] [CrossRef]
- Zhang, L.; Zhou, W.D.; Li, F.Z. Kernel sparse representation-based classifier ensemble for face recognition. Multimed. Tools Appl. 2015, 74, 123–137. [Google Scholar] [CrossRef]
- Zhu, P.; Zhang, L.; Hu, Q.; Shiu, S.C.K. Multi-scale patch based collaborative representation for face recognition with margin distribution optimization. In Proceedings of the European Conference on Computer Vision, Florence, Italy, 7–13 October 2012; pp. 822–835. [Google Scholar]
- Cheng, J.; Chen, L. A Weighted Regional Voting Based Ensemble of Multiple Classifiers for Face Recognition. In Proceedings of the International Symposium on Visual Computing, Las Vegas, NV, USA, 8–10 December 2014; pp. 482–491. [Google Scholar]
- Sidiropoulos, N.D.; Lathauwer, L.D.; Fu, X.; Huang, K.; Papalexakis, E.E.; Faloutsos, C. Tensor Decomposition for Signal Processing and Machine Learning. IEEE Trans. Signal Process. 2017, 65, 3551–3582. [Google Scholar] [CrossRef]
- Phan, A.H.; Cichocki, A. Tensor decompositions for feature extraction and classification of high dimensional datasets. Nonlinear Theory Its Appl. Ieice 2010, 1, 37–68. [Google Scholar] [CrossRef] [Green Version]
- Pfeifer, R.N.C.; Evenbly, G.; Singh, S.; Vidal, G. NCON: A tensor network contractor for MATLAB. arXiv, 2014; arXiv:1402.0939. [Google Scholar]
- Cichocki, A.; Mandic, D.; Lathauwer, L.D.; Zhou, G.; Zhao, Q.; Caiafa, C.; Phan, H.A. Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis. IEEE Signal Process. Mag. 2014, 32, 145–163. [Google Scholar] [CrossRef]
- Russell, S.J.; Norvig, P.N. Artificial Intelligence: A Modern Approach. Prentice Hall. Appl. Mech. Mater. 2003, 263, 2829–2833. [Google Scholar]
- Shi, Y.; Eberhart, R.C. Empirical study of particle swarm optimization. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, Washington, DC, USA, 6–9 July 1999; Volume 321, pp. 320–324. [Google Scholar]
- Wang, X.; Han, T.X.; Yan, S. An HOG-LBP human detector with partial occlusion handling. In Proceedings of the 2009 IEEE International Conference on Computer Vision, Kyoto, Japan, 29 September–2 October 2009; pp. 32–39. [Google Scholar]
- Ameur, B.; Masmoudi, S.; Derbel, A.G.; Hamida, A.B. Fusing Gabor and LBP feature sets for KNN and SRC-based face recognition. In Proceedings of the 2016 International Conference on Advanced Technologies for Signal and Image Processing, Monastir, Tunisia, 21–23 March 2016; pp. 453–458. [Google Scholar]
- Lyons, M.; Akamatsu, S.; Kamachi, M.; Gyoba, J. Coding facial expressions with Gabor wavelets. In Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan, 14–16 April 1998; pp. 200–205. [Google Scholar] [Green Version]
- Yang, M.; Zhang, L.; Shiu, S.C.K.; Zhang, D. Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary. Pattern Recognit. 2013, 46, 1865–1878. [Google Scholar] [CrossRef] [Green Version]
- Ojala, T.; Harwood, I. A Comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recognit. 1996, 29, 51–59. [Google Scholar] [CrossRef]
- Ojala, T.; Pietikainen, M.; Maenpaa, T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24, 971–987. [Google Scholar] [CrossRef] [Green Version]
- Duda, R.O.; Hart, P.E. Pattern Classification and Scene Analysis; Wiley: Hoboken, NJ, USA, 1973; pp. 119–131. [Google Scholar]
- Du, B.; Zhang, M.; Zhang, L.; Hu, R.; Tao, D. PLTD: Patch-Based Low-Rank Tensor Decomposition for Hyperspectral Images. IEEE Trans. Multimed. 2017, 19, 67–79. [Google Scholar] [CrossRef]
- Peng, Y.; Meng, D.; Xu, Z.; Gao, C.; Yang, Y.; Zhang, B. Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23–28 June 2014; pp. 2949–2956. [Google Scholar]
- Saito, N. Simultaneous Noise Suppression and Signal Compression Using a Library of Orthonormal Bases and the Minimum-Description-Length Criterion; Academic Press: Cambridge, MA, USA, 1994; pp. 299–324. [Google Scholar]
- Averbuch, A.; Coifman, R.R.; Donoho, D.L.; Elad, M.; Israeli, M. Fast and accurate Polar Fourier transform. Appl. Comput. Harmon. Anal. 2006, 21, 145–167. [Google Scholar] [CrossRef]
- Gonzalez, R.C.; Woods, R.E. Digital Image Processing; Prentice Hall International: Upper Saddle River, NJ, USA, 1992; Volume 28, pp. 484–486. [Google Scholar]
- Kittler, J.; Hatef, M.; Duin, R.P.W.; Matas, J. On Combining Classifiers. In Proceedings of the International Conference on Pattern Recognition, Vienna, Austria, 25–29 August 1996. [Google Scholar]
- Ellis, H.D.; Frse, M.A.J.; Newcombe, F.; Young, A. Aspects of Face Processing; Springer Science & Business Media: Berlin, Germany, 1986. [Google Scholar]
- Su, Y.; Shan, S.; Chen, X.; Gao, W. Hierarchical Ensemble of Global and Local Classifiers for Face Recognition. IEEE Trans. Image Process. 2009, 18, 1885–1896. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Eberhart, R.; Kennedy, J. A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 4–6 October 1995; pp. 39–43. [Google Scholar]
- Mao, S.; Xiong, L.; Jiao, L.C.; Zhang, S.; Chen, B. Weighted ensemble based on 0–1 matrix decomposition. Electron. Lett. 2013, 49, 116–118. [Google Scholar] [CrossRef]
- Robila, S.A. Toward hyperspectral face recognition. Proc. SPIE 2008, 6812, 68120. [Google Scholar]
- Chang, C.C.; Lin, C.J. A Library for Support Vector Machines. ACM Trans. Intell. Syst. Technol. 2011, 2. [Google Scholar] [CrossRef]
- Zhang, L.; Yang, M. Sparse representation or collaborative representation: Which helps face recognition? In Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain, 6–13 November 2011; pp. 471–478. [Google Scholar]
Local Feature | Eyebrow | Nose | Mouth | Eyebrows and Eyes | Eyes and Nose |
---|---|---|---|---|---|
The number of atoms | [14, 31, 2] | [15, 15, 2] | [36, 15, 2] | [36, 36, 12] | [19, 19, 3] |
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Wu, M.; Wei, D.; Zhang, L.; Zhao, Y. Hyperspectral Face Recognition with Patch-Based Low Rank Tensor Decomposition and PFFT Algorithm. Symmetry 2018, 10, 714. https://doi.org/10.3390/sym10120714
Wu M, Wei D, Zhang L, Zhao Y. Hyperspectral Face Recognition with Patch-Based Low Rank Tensor Decomposition and PFFT Algorithm. Symmetry. 2018; 10(12):714. https://doi.org/10.3390/sym10120714
Chicago/Turabian StyleWu, Mengmeng, Dongmei Wei, Liren Zhang, and Yuefeng Zhao. 2018. "Hyperspectral Face Recognition with Patch-Based Low Rank Tensor Decomposition and PFFT Algorithm" Symmetry 10, no. 12: 714. https://doi.org/10.3390/sym10120714