Abstract
In video surveillance and others real-life applications, it is usually needed to match low resolution (LR) face images against high-resolution (HR) gallery images. Although extensive efforts have been made, it is still difficult to find effective representations for low-resolution face recognition due to the degradation in resolution together with facial variations. This paper makes use of alternative representations based on dissimilarities between objects. Unlike previous works, we construct the dissimilarity space on top of deep convolutional features. We obtain a more compact representation by using prototype selection methods. Besides, metric learning methods are used to replace the standard Euclidean distance in the dissimilarity space. Experiments conducted on two data sets particularly designed for low-resolution face recognition showed that the proposal outperforms state-of-the-art methods, including some neural networks designed for this problem.
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References
Grgic, M., Delac, K., Grgic, S.: SCface-surveillance cameras face database. Multimed. Tools Appl. 51(3), 863–879 (2011)
Biswas, S., Aggarwal, G., Flynn, P.J., Bowyer, K.W.: Pose-robust recognition of low-resolution face images. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 3037–3049 (2013)
Zeng, D., Chen, H., Zhao, Q.: Towards resolution invariant face recognition in uncontrolled scenarios. In: 2016 International Conference on Biometrics (ICB), pp. 1–8. IEEE (2016)
Lu, T., Xiong, Z., Zhang, Y., Wang, B., Lu, T.: Robust face super-resolution via locality-constrained low-rank representation. IEEE Access 5, 13103–13117 (2017)
Rajawat, A., Pandey, M.K., Rajput, S.S.: Low resolution face recognition techniques: a survey. In: 2017 3rd International Conference on Computational Intelligence and Communication Technology (CICT), pp. 1–4. IEEE (2017)
Hernández-Durán, M., Plasencia-Calaña, Y., Méndez-Vázquez, H.: Metric learning in the dissimilarity space to improve low-resolution face recognition. In: Beltrán-Castañón, C., Nyström, I., Famili, F. (eds.) CIARP 2016. LNCS, vol. 10125, pp. 217–224. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52277-7_27
Pękalska, E., Duin, R.P., Paclík, P.: Prototype selection for dissimilarity-based classifiers. Pattern Recognit. 39(2), 189–208 (2006)
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. BMVC 1, 6 (2015)
López-Avila, L., Plasencia-Calaña, Y., Martínez-Díaz, Y., Méndez-Vázquez, H.: On the use of pre-trained neural networks for different face recognition tasks. In: Mendoza, M., Velastín, S. (eds.) CIARP 2017. LNCS, vol. 10657, pp. 356–364. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75193-1_43
Pękalska, E., Duin, R.: Dissimilarity Representation For Pattern Recognition: Foundations and Applications, vol. 64. World Scientific, Singapore (2005)
Plasencia-Calaña, Y., Orozco-Alzate, M., Méndez-Vázquez, H., García-Reyes, E., Duin, R.P.W.: Towards scalable prototype selection by genetic algorithms with fast criteria. In: Fränti, P., Brown, G., Loog, M., Escolano, F., Pelillo, M. (eds.) S+SSPR 2014. LNCS, vol. 8621, pp. 343–352. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44415-3_35
Olivetti, E., Nguyen, T.B., Garyfallidis, E.: The approximation of the dissimilarity projection. In: 2012 International Workshop on Pattern Recognition in NeuroImaging (PRNI), pp. 85–88. IEEE (2012)
Hastie, T., Tibshirani, R.: Discriminant adaptive nearest neighbor classification and regression. In: Advances in Neural Information Processing Systems, pp. 409–415 (1996)
Weinberger, K.Q., Blitzer, J., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. In: Advances in neural information processing systems, pp. 1473–1480 (2006)
Huang, Z., et al.: A Benchmark and comparative study of video-based face recognition on COX face database. IEEE Trans. Image Process. 24(12), 5967–5981 (2015)
Huang, Z., Wang, R., Van Gool, L., Chen, X., et al.: Cross euclidean-to-riemannian metric learning with application to face recognition from video. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
Liao, S., Lei, Z., Yi, D., Li, S.Z.: A Benchmark study of large-scale unconstrained face recognition. In: 2014 IEEE International Joint Conference on Biometrics (IJCB), pp. 1–8. IEEE (2014)
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Hernández-Durán, M., Plasencia-Calaña, Y., Méndez-Vázquez, H. (2018). Low-Resolution Face Recognition with Deep Convolutional Features in the Dissimilarity Space. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science(), vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_11
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