张海军, 穆志纯, 张成阳. 基于ICA和BP神经网络的人耳图像识别[J]. 工程科学学报, 2006, 28(6): 600-603. DOI: 10.13374/j.issn1001-053x.2006.06.020
引用本文: 张海军, 穆志纯, 张成阳. 基于ICA和BP神经网络的人耳图像识别[J]. 工程科学学报, 2006, 28(6): 600-603. DOI: 10.13374/j.issn1001-053x.2006.06.020
ZHANG Haijun, MU Zhichun, ZHANG Chengyang. Ear recognition method based on independent component analysis and BP neural network[J]. Chinese Journal of Engineering, 2006, 28(6): 600-603. DOI: 10.13374/j.issn1001-053x.2006.06.020
Citation: ZHANG Haijun, MU Zhichun, ZHANG Chengyang. Ear recognition method based on independent component analysis and BP neural network[J]. Chinese Journal of Engineering, 2006, 28(6): 600-603. DOI: 10.13374/j.issn1001-053x.2006.06.020

基于ICA和BP神经网络的人耳图像识别

Ear recognition method based on independent component analysis and BP neural network

  • 摘要: 提出了一种独立分量分析和BP神经网络相结合的人耳识别新方法(ICABP法).首先采用快速独立分量分析方法提取人耳图像的独立基图像和投影向量,然后采用改进的三层BP神经网络进行分类识别.该方法将ICA的空间局部特征提取功能和BP神经网络的自适应功能有机地结合起来,增强了系统的鲁棒性.实验表明,ICABP法取得了很高的识别率.

     

    Abstract: A new ear recognition method combining independent component analysis (ICA) and BP neural network was proposed. The FastICA algorithm was used to derive independent basic images and projection vectors out of ear images, and three-layer BP neural network was used to classify ears. The local features extraction of ICA and the adaptability of BP neural network were combined reasonably. The robustness of the system was enhanced. Experiment results show that the ear recognition rate of the ICA-BP method is improved obviously.

     

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