Abstract
According to the limitation of 2D or 3D ear recognition and the complementarity between two recognition strategies, a multimodal ear recognition method based on 2D and 3D ear feature-level fusion is presented in this paper. Firstly, LGBP algorithm is used to describe textural feature of 2D ear and depth feature of 3D ear respectively. Then two feature vectors are concatenated to form a high dimensional fused feature. Finally, the KPCA+ReliefF method is proposed to eliminate the correlation between 2D and 3D ear images and remove the redundancy data. Experimental results show that the multimodal ear recognition outperforms either using 2D or 3D alone, especially under illumination variation, partial occlusion and posture change.
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Guo, M., Mu, Z., Yuan, L. (2012). Multimodal Ear Recognition Based on 2D+3D Feature Fusion. In: Zheng, WS., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_28
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DOI: https://doi.org/10.1007/978-3-642-35136-5_28
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