Abstract
In a recent paper, a multi-scale information fusion method was presented to improve LBP for face identification. However, the additional parameters employed in that method cannot be automatically optimised. In this paper, a novel self-adaptive feature fusion method is proposed which extends the mLBP method by removing the need to optimise these parameters. Our method involves four steps. Firstly, a large number of initial features are generated. Then, we proposed a Fisher criteria-based method for evaluating the discriminative capabilities of different feature groups. After that, we proposed a model based on prism volume for selecting the optimal parameter combination. Finally, the resulting multi-scale feature are fused by a extended Euclidean distance fusion. Extensive experiments on two face databases have shown the proposed self-adaptive feature fusion method can find parameters that are optimal to the data in question, and can produce excellent classification performance.
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Wei, X., Wang, H., Wan, H., Sctoney, B. (2017). Self-adaptive Feature Fusion Method for Improving LBP for Face Identification. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_33
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DOI: https://doi.org/10.1007/978-3-319-68345-4_33
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