Abstract
A novel face recognition scheme based on multi-stages classifier, which includes methods of support vector machine (SVM), Eigenface, and random sample consensus (RANSAC), is proposed in this paper. The whole decision process is conducted cascade coarse-to-fine stages. The first stage adopts one-against-one-SVM (OAO-SVM) method to choose two possible classes best similar to the testing image. In the second stage, “Eigenface” method was employed to select one prototype image with the minimum distance to the testing image in each of the two classes chosen. Finally, the real class is determined by comparing the geometric similarity, as done by “RANSAC” method, between these prototype images and the testing images. This multi-stage face recognition system has been tested on Olivetti Research Laboratory (ORL) face databases, and its experimental results give evidence that the proposed approach outperforms the other approaches either based on the single classifier or multi-parallel classifier, it can even obtain a nearly 100 percent recognition accuracy.
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References
Er, M.J., Chen, W., Wu, S.: High-Speed Face Recognition Based on Discrete Cosine Transform and RBF Neural Networks. IEEE Trans. Neural Networks 16(3), 679–691 (2005)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)
Xiang, C., Fan, X.A., Lee, T.H: Face Recognition Using Recursive Fisher Linear Discriminant. IEEE Trans. on Image Processing 15(8), 2097–2105 (2006)
Guo, G., Li, S.Z., Chan, K.L.: Support vector machines for face recognition. Image and Vision Computing 19, 631–638 (2001)
Othman, H., Aboulnasr, T.: A Separable Low Complexity 2D HMM with Application to Face Recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(10), 1229–1238 (2003)
Lu, J.K., Plataniotis, N.A., Venetsanopoulos, N., Li, S.Z.: Ensemble-Based Discriminant Learning With Boosting for Face Recognition. IEEE Trans. on Neural Networks 17(1), 166–178 (2006)
Kwak, K.C., Pedrycz, W.: Face recognition: A study in information fusion using fuzzy integral. Pattern Recognition Letters 26, 719–733 (2005)
Rajagopalan, A.N., Rao, K.S., Kumar, Y.A.: Face recognition using multiple facial features. Pattern Recognition Letters 28, 335–341 (2007)
Zhou, D., Yang, X., Peng, N., Wang, Y.: Improved-LDA based face recognition using both facial global and local information. Pattern Recognition Letters 27, 536–543 (2006)
Zhao, Z.Q., Huang, D.S., Sun, B.Y.: Human face recognition based on multi-features using neural networks committee. Pattern Recognition Letters 25, 1351–1358 (2004)
Lemieux, A., Parizeau, M.: Flexible multi-classifier architecture for face recognition systems. In: 16th Int. Conf. on Vision Interface (2003)
Hsu, C.W., Lin, C.J.: A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Network 13(2), 415–425 (2002)
Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, Inc., Chichester (1998)
Bottou, L., Cortes, C., Denker, J., Drucker, H., Guyon, I., Jackel, L., LeCun, Y., Muller, U., Sackinger, E., Simard, P., Vapnik, V.: Comparison of classifier methods: a case study in handwriting digit recognition. In: International Conference on Pattern Recognition, pp. 77–87. IEEE Computer Society Press, Los Alamitos (1994)
Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large margin DAGs for multiclass classification. In: Advances in Neural Information Processing Systems, vol. 12, pp. 547–553. MIT Press, Cambridge (2000)
Ratsch, G., Onoda, T., Muller, K.R.: Soft Margins for AdaBoost. Machine Learning 42, 287–320 (2001)
Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24(6), 381–395 (1981)
ORL face database, http://www.uk.research.att.com/facedatabase.html
Harris, C., Stephens, M.: A Combined Corner and Edge Detector. In: 4th Alvey Vision Conference, pp. 147–151 (1988)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2003)
Yang, J., Zhang, D., Frangi, A.F., Yang, J.Y.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 26, 131–137 (2004)
Kwak, K.C., Pedrycz, W.: Face recognition using a fuzzy fisferface classifier. Pattern Recognition 38, 1717–1732 (2005)
Li, B., Liu, Y.: When eigenfaces are combined with wavelets. Knowledge-Based Systems 15, 343–347 (2002)
Phiasai, T., Arunrungrusmi, S., Chamnongthai, K.: Face recognition system with PCA and moment invariant method. In: Proc. of the IEEE International Symposium on Circuits and Systems, vol. 2, pp. 165–168 (2001)
Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face recognition using LDA-based algorithms. IEEE Trans. on Neural Networks 14, 195–200 (2003)
Chien, J.T., Wu, C.C.: Discriminant waveletfaces and nearest feature classifiers for face recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 24, 1644–1649 (2002)
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Kuo, CH., Lee, JD., Chan, TJ. (2007). A Novel Multi-stage Classifier for Face Recognition. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_62
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DOI: https://doi.org/10.1007/978-3-540-76390-1_62
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