Abstract
A novel approach for face recognition via domain adaptation and manifold distance metric learning is presented in this paper. Recently, unconstrained face recognition is becoming a research hot in computer vision. For the non-independent and identically distributed data set, the maximum mean discrepancy algorithm in domain adaption learning is used to represent the difference between the training set and the test set. At the same time, assume that the same type of face data are distributed on the same manifold and the different types of face data are distributed on different manifolds, the face image set is used to model multiple manifolds and the distance between affine hulls is used to represent the distance between manifolds. At last, a projection matrix will be explored by maximizing the distance between manifolds and minimizing the difference between the training set and test set. A large number of experimental results on different face data sets show the efficiency of the proposed method.
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Acknowledgments
This work was supported in part by the grants of Natural Science Foundation of China (61572381, 61273303, 61472280 and 61602349) and Post-doctoral Science Foundation of China (2016M601646).
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Li, B., Zheng, PP., Liu, J., Zhang, XL. (2017). Face Recognition via Domain Adaptation and Manifold Distance Metric Learning. In: Huang, DS., Jo, KH., Figueroa-GarcÃa, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_1
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DOI: https://doi.org/10.1007/978-3-319-63312-1_1
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