Abstract
This paper presents a novel approach to face recognition, named Multi-resolution Histograms of Local Variation Patterns (MHLVP), in which face images are represented as the concatenation of the local spatial histogram of local variation patterns computed from the multi-resolution Gabor features. For a face image with abundant texture and shape information, aGabor feature map(GFM) is computed by convolving the image with each of the forty multi-scale and multi-orientation Gabor filters. Each GFM is then divided into small non-overlapping regions to enhance its shape information, and then Local Binary Pattern (LBP) histograms are extracted for each region and concatenated into a feature histogram to enhance the texture information in the specific GFM. Further more, all of the feature histograms extracted from the forty GFMs are further concatenated into a single feature histogram as the final representation of the given face image. Eventually, the identification is achieved by histogram intersection operation. Our experimental results on FERET face databases show that the proposed method performs terrifically better than the performance of some classical results including the best results in FERET’97.
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References
Phillips, P.J., Grother, P., Micheals, R.J., Blackburn, D.M., Tabassi, E., Bone, J.M.: Face recognition vendor test 2002 results. Technical report (2003)
Chellappa, R., Wilson, C.L., Sirohey, S.: Human and Machine Recognition of faces: A survey. Proc. of the IEEE 83, 705–740 (1995)
Zhao, W.Y., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Survey, 399–458 (2003)
Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing 16, 295–306 (1998)
Turk, M., Pentland, A.: Face recognition using eigenfaces. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)
Belhumer, P., Hespanha, P., Kriegman, D.: Eigenfaecs vs fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)
Swets, D.L., Weng, J.: Using Discriminant eigenfeatures for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 831–836 (1996)
Moghaddam, B., Nastar, C., Pentland, A.: A Bayesian similarity measure for direct image matching. In: 13th International Conference on Pattern Recognition, pp. II: 350–358 (1996)
Hadjidemetriou, E., Grossberg, M.D., Nayar, S.K.: Multiresolution Histograms and Their Use for Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 831–847 (2004)
Hadjidemetriou, E., Grossberg, M.D., Nayar, S.K.: Spatial Information in Multiresolution Histograms. In: Proc. Computer Vision and Pattern Recognition Conf., pp. I: 702–709 (2001)
Porat, M., Zeevi, Y.: The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 10, 452–468 (1988)
Liu, C.J., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant modal for face recognition image processing. IEEE Transactions on Image Process 11, 467–476 (2002)
Lades, M., Vorbrüggen, J.C., Buhmann, J., Lange, J., Malsburg, C., Würtz, R.P., Konen, W.: Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions on Computers 42, 300–311 (1993)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29, 51–59 (1996)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)
Daugman, J.G.: Two-dimensional spectral analysis of cortical receptive field profile. Vision Research 20, 847–856 (1980)
Swain, M., Ballard, D.: Color indexing. Int.J.Computer Vision 7, 11–32 (1991)
Brunelli, R., Poggio, T.: Face Recognition: Features vs. Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1042–1053 (1993)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley Interscience, USA (2001)
Phillips, P.J., Syed, H.M., Rizvi, A., Rauss, P.J.: The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1090–1104 (2000)
Timo, A., Abdenour, H., Matti, P.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
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Zhang, W., Shan, S., Zhang, H., Gao, W., Chen, X. (2005). Multi-resolution Histograms of Local Variation Patterns (MHLVP) for Robust Face Recognition. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_98
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DOI: https://doi.org/10.1007/11527923_98
Publisher Name: Springer, Berlin, Heidelberg
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