Abstract
This paper proposes a novel face representation and recognition method based on local Gabor textons. Textons, defined as a vocabulary of local characteristic features, are a good description of the perceptually distinguishable micro-structures on objects. In this paper, we incorporate the advantages of Gabor feature and textons strategy together to form Gabor textons. And for the specificity of face images, we propose local Gabor textons (LGT) to portray faces more precisely and efficiently. The local Gabor textons histogram sequence is then utilized for face representation and a weighted histogram sequence matching mechanism is introduced for face recognition. Preliminary experiments on FERET database show promising results of the proposed method.
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Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. In: Proceedings of the European Conference on Computer Vision, Prague, Czech, pp. 469–481 (2004)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. PAMI 19(7), 711–720 (1997)
Comon, P.: Independent component analysis - a new concept? Signal Processing 36, 287–314 (1994)
Cula, O., Dana, K.: Compact representation of bidirectional texture functions. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1041–1047. IEEE Computer Society Press, Los Alamitos (2001)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, Chichester (2000)
Julesz, B.: Texton, the elements of texture perception, and their interactions 290(5802), 91–97 (March 1981)
Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision 43(1), 29–44 (2001)
Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 11(4), 467–476 (2002)
Moghaddam, B., Jebara, T., Pentland, A.: Bayesian face recognition. Pattern Recognition 33(11), 1771–1782 (2000)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Varma, M., Zisserman, A.: Classifying images of materials: achieving viewpoint and illumination independence. In: Proceedings of the European Conference on Computer Vision, pp. 255–271 (2002)
Wiskott, L., Fellous, J., Kruger, N., malsburg, C.V.: Face recognition by elastic bunch graph matching. IEEE Trans. PAMI 19(7), 775–779 (1997)
Zhang, W.C., Shan, S.G., Gao, W., Zhang, H.M.: Local gabor binary pattern histogram sequence (lgbphs): a novel non-statistical model for face representation and recognition. In: Proceedings of IEEE International Conference on Computer Vision, pp. 786–791. IEEE Computer Society Press, Los Alamitos (2005)
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Lei, Z., Li, S.Z., Chu, R., Zhu, X. (2007). Face Recognition with Local Gabor Textons. In: Lee, SW., Li, S.Z. (eds) Advances in Biometrics. ICB 2007. Lecture Notes in Computer Science, vol 4642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74549-5_6
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DOI: https://doi.org/10.1007/978-3-540-74549-5_6
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