Abstract
Sparse signal representation proposes a novel insight to solve face recognition problem. Based on the sparse assumption that a new object can be sparsely represented by other objects, we propose a simple yet efficient direct sparse nearest feature classifier to deal with the problem of automatically real-time face recognition. Firstly, we present a new method, which calculates an approximate sparse code to alleviate the extrapolation and interpolation inaccuracy in nearest feature classifier. Secondly, a sparse score normalization method is developed to normalize the calculated scores and to achieve a high receiver operator characteristic (ROC) curve. Experiments on FRGC and PIE face databases show that our method can get comparable results against sparse representation-based classification on both recognition rate and ROC curve.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Li, S.Z., Lu, J.: Face recognition using nearest feature line method. IEEE Trans. Neural Network 10(2), 439–443 (1999)
Li, S.Z.: Face Recognition Based on Nearest Linear Combinations. In: CVPR (1998)
Chien, J.T., Wu, C.C.: Discriminant waveletfaces and nearest feature classifiers for face recognition. IEEE Trans. PAMI 24(12), 1644–1649 (2002)
Zhang, J., Li, S.Z., Wang, J.: Nearest manifold approach for face recognition. Automatic Face and Gesture Recognition, 223– 228 (2004)
Vincent, P., Bengio, Y.: K-Local Hyperplane and. Convex Distance Nearest-Neighbor Algorithms. In: Advances in Neural Information Processing Systems, vol. 14, pp. 985–992 (2001)
Ho, J., Yang, M., Lim, J., Lee, K., Kriegman, D.: Clustering appearances of objects under varying illumination conditions. In: CVPR, pp. 11–18 (2003)
Zhan, D.-C., Zhou, Z.-H.: Neighbor line-based locally linear embedding. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 606–615. Springer, Heidelberg (2006)
Liu, W., Wang, Y.H., Li, S.Z., Tan, T.N.: Nearest intra-class space classifier for face recognition. In: Proceedings of the 17th ICPR, pp. 495–498 (2004)
He, Y.H.: Face Recognition Using Kernel Nearest Feature Classifiers. In: Int. Con. on Computational Intelligence and Security, vol. 1, pp. 678–683 (2006)
Du, H., Chen, Y.Q.: Rectified nearest feature line segment for pattern classification. Pattern Recognition 40(5), 1486–1497 (2007)
Pang, Y., Yuan, Y., Li, X.: Generalized nearest feature line for subspace learning. IEE Electronics Letters 43(20), 1079–1080 (2007)
Orozco-Alzate, M., Duin, R.P.W., Casteiianos-Domingue, C.S.: Generalizing Dissimilarity Representations Using Feature lines. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 370–379. Springer, Heidelberg (2007)
Zheng, W.M., Zhao, L., Zou, C.R.: Locally nearest neighbor classifiers for pattern classification. Pattern Recognition 37, 1307–1309 (2004)
Naseem, I., Togneri, R., Bennamoun, M.: Linear regression for face recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence (2009) (accepted)
Ran, B.G., Hu, Zeng, W.S., Guo, Y.Q.: Two-stage Sparse Representation for Robust Recognition on Large-scale Database. In: Twenty- Fourth AAAI Conference on Artificial Intelligence (AAAI 2010) (2010)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. PAMI (March 2008)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)
Candes, E., Romberg, J.: l 1-magic: Recovery of sparse signals via convex programming (2005), http://www.acm.caltech.edu/l1magic/
Martinez, A., Benavente, R.: The AR face database. CVC Tech. Report 24 (1998)
Turk, M., Pentland, A.: Eigenfaces for recognition. In: CVPR (1991)
Belhumeur, P., Hespanda, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Trans. on PAMI Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)
Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and Expression Database. IEEE Transactions on PAMI 25(12), 1615–1618 (2003)
Philips, P., Flynn, P., Sruggs, T., Bowyer, K.: Overview of the face recognition grand challenge. In: CVPR (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, R., Yang, N., Wang, XK., Tan, GZ. (2010). Direct Sparse Nearest Feature Classifier for Face Recognition. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science(), vol 6330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15615-1_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-15615-1_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15614-4
Online ISBN: 978-3-642-15615-1
eBook Packages: Computer ScienceComputer Science (R0)