Gabor face recognition by multi-channel classifier fusion of supervised kernel manifold learning
Gabor face recognition by multi-channel classifier fusion of supervised kernel manifold learning
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DOI：
10.1016/j.neucom.2012.05.005
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发表时间：
2012-11
期刊：
影响因子：
6
通讯作者：
Zeng-Shun Zhao;Li Zhang;Meng Zhao;Z. Hou;Changshui Zhang
中科院分区：
文献类型：
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作者：
Zeng-Shun Zhao;Li Zhang;Meng Zhao;Z. Hou;Changshui Zhang
基于Gabor特征表示的多通道特性和多分类器融合的成功性，同时避免了流形学习参数的选择，提出了一种多通道融合策略下的人脸识别框架。Gabor小波赋予算法类似于人类视觉系统的方式来表示人脸特征。为了解决多通道Gabor特征带来的维数灾难，同时保持样本点的非线性标记内在结构，采用流形学习对非线性标记内在结构进行建模。每个滤波后的多通道Gabor特征被视为一个独立的通道。分量分类器对每个通道进行分类，并采用决策融合策略得到最终结果。在三个人脸数据集上的实验表明，与现有的方法相比，该方法具有有效的识别精度。
Motivated by the multi-channel nature of the Gabor feature representation and the success of the multiple classifier fusion, and meanwhile, to avoid careful selection of parameters for the manifold learning, we propose a face recognition framework under the multi-channel fusion strategy. The Gabor wavelet endows the algorithm in a similar way as the human visual system, to represent face features. To solve the curse of dimensionality due to multi-channel Gabor feature, as well as to preserve nonlinear labeled intrinsic structure of the sample points, the manifold learning is applied to model the nonlinear labeled intrinsic structure. Each of the filtered multi-channel Gabor features, is treated as an independent channel. Classification is performed in each channel by the component classifier and the final result is obtained using the decision fusion strategy. The experiments on three face datasets show effective and encouraging recognition accuracy compared with other existing methods.