基于ICA的非线性自适应特征融合的人耳识别

An ICA-Based Ear Recognition Method through Nonlinear Adaptive Feature Fusion

针对单一特征的人耳识别对旋转角度鲁棒性差的问题,提出一种非线性自适应特征融合的方法.首先提取人耳的2种具有互补性质的独立成分特征,然后将它们加权串联形成高维融合特征;最后通过核主元分析方法实现非线性降维.实验结果表明,当人耳有姿态旋转时,融合特征较单一特征的识别率有显著提升,且文中方法比传统的串联融合的识别结果更好.

The performance of ear recognition based only on one type of features could be very poor when the ear has a large pose variation. To tackle the problem, we propose a nonlinear adaptive feature fusion method. Firstly, two types of complimentary features .are extracted using ICA. Then thoes features under different weighting are concatenated to form a high dimensional fused feature. Finally, the feature dimension is reduced by the kernel PCA. Experimental results show that the ear recognition rate with our fused feature is much higher under large pose variation. In addition, the fusion strategy we proposed here works even better than the conventional serial feature fusion one.