Abstract
Face recognition systems aimed at working on large scale datasets are required to solve specific hurdles. In particular, due to the huge amount of data, it becomes mandatory to furnish a very fast and effective approach. Moreover the solution should be scalable, that is it should deal efficiently the growing of the gallery with new subjects. In literature, most of the works tackling this problem are composed of two stages, namely the selection and the classification. The former is aimed at significantly pruning the face image gallery, while the latter, often expensive but precise, determines the probe identity on this reduced domain. In this article a new selection method is presented, combining a multi-feature representation and the least squares method. Data are split into sub-galleries so as to make the system more efficient and scalable. Experiments on the union of four challenging datasets and comparisons with the state-of-the-art prove the effectiveness of our method.
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Abate, A.F., Nappi, M., Riccio, D., Sabatino, G.: 2d and 3d face recognition: A survey. Pattern Recognition Letters 28(14), 1885–1906 (2007)
Adamo, A., Grossi, G., Lanzarotti, R.: Sparse Representation Based Classification for Face Recognition by k-LiMapS Algorithm. In: Elmoataz, A., Mammass, D., Lezoray, O., Nouboud, F., Aboutajdine, D. (eds.) ICISP 2012. LNCS, vol. 7340, pp. 245–252. Springer, Heidelberg (2012)
Borgi, M.A., Labate, D., El’Arbi, M., Ben Amar, C.: Shearlet Network-based Sparse Coding Augmented by Facial Texture Features for Face Recognition. In: Petrosino, A. (ed.) ICIAP 2013, Part II. LNCS, vol. 8157, pp. 611–620. Springer, Heidelberg (2013)
Cuculo, V., Lanzarotti, R., Boccignone, G.: Using sparse coding for landmark localization in facial expressions. In: Proc. of Int’l Conf. EUVIP. IEEE (2014)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (2005)
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. Image Vision Comput. 28(5), 807–813 (2010)
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Tech. Rep. 07–49, University of Massachusetts, Amherst (October 2007)
Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and Simile Classifiers for Face Verification. In: IEEE Int’l Conf. on Computer Vision (ICCV) (2009)
Kyperountas, M., Tefas, A., Pitas, I.: Face recognition via adaptive discriminant clustering. In: Proc. of the Int’l Conf. on Image Processing, ICIP 2008, pp. 2744–2747 (2008)
Li, C., Guo, J., Zhang, H.: Local sparse representation based classification. In: 20th International Conference on Pattern Recognition, ICPR 2010, pp. 649–652 (2010)
Liu, W., Wang, Y., Li, S., Tan, T.: Null space-based kernel fisher discriminant analysis for face recognition. In: Int’l Conf. on Automatic Face and Gesture Recognition, pp. 369–374. IEEE (2004)
Lu, J., Plataniotis, K.: Boosting face recognition on a large-scale database. In: Int’l Conf. on Image Processing, pp. 109–112. IEEE (2002)
Martinez, A., Benavente, R.: The AR face database. CVC Tech. Rep. 24 (1998)
Nan, Z., Jian, Y.: K nearest neighbor based local sparse representation classifier. IEEE 2010 Chin. Conf. on Pattern Recognition, CCPR, pp. 1–5 (2010)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Recognition and Machine Intelligence 24(7), 971–987 (2002)
Ortiz, E.G., Becker, B.C.: Face recognition for web-scale datasets. Computer Vision and Image Understanding 118, 153–170 (2014)
Phillips, P., Flynn, P., Scruggs, T., Bowyer, K.: Overview of the face recognition grand challenge. Proc. IEEE Conf. CVPR 1, 947–954 (2005)
Schwartz, W., Guo, H., Choi, J., Davis, L.: Face identification using large feature sets. IEEE Transactions on Image Processing 21(4), 2245–2255 (2012)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. Trans. on Image Processing 19, 1635–1650 (2010)
Štruc, V., Pavešić, N.: Photometric normalization techniques for illumination invariance. In: Zhang, Y. (ed.) Advances in Face Image Analysis: Techniques and Technologies, pp. 279–300. IGI-Global (2011)
Wolf, L., Hassner, T., Taigman, Y.: Descriptor based methods in the wild. In: European Conference on Computer Vision (ECCV) (2008)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Analysis and Machine Intelligence 31(2), 210–27 (2008)
Yan, J., Lei, Z., Yi, D., Li, S.: Towards incremental and large scale face recognition. In: Proc. of Int’l Joint Conference on Biometrics (IJCB 2011), pp. 1–6 (2011)
Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35(4), 399–458 (2003)
Zheng, W., Hu, B., Kong, X.: Two-stage nonnegative sparse representation for large-scale face recognition. IEEE Trans. on Neural Networks and Learning Systems 24(1), 35–46 (2013)
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Grossi, G., Lanzarotti, R., Lin, J. (2015). A Selection Module for Large-Scale Face Recognition Systems. In: Murino, V., Puppo, E. (eds) Image Analysis and Processing — ICIAP 2015. ICIAP 2015. Lecture Notes in Computer Science(), vol 9280. Springer, Cham. https://doi.org/10.1007/978-3-319-23234-8_49
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