目的高光谱人脸数据具有丰富的鉴别信息。最优谱带选择和谱内间特征表示是高光谱人脸识别的关键。基于高光谱波段范围为4001090 nm和采样间隔为10 nm的高光谱成像人脸数据,本文提出一种分块谱带选择和VGG(Visual Geometry Group)网络的...目的高光谱人脸数据具有丰富的鉴别信息。最优谱带选择和谱内间特征表示是高光谱人脸识别的关键。基于高光谱波段范围为4001090 nm和采样间隔为10 nm的高光谱成像人脸数据,本文提出一种分块谱带选择和VGG(Visual Geometry Group)网络的高光谱人脸识别方法。方法为了优化适合人脸识别的谱带组合,基于人脸关键点,提出分块局部二值模式(local binary pattern,LBP)特征的Ada Boost支持向量机(support vector machine,SVM)谱带选择方法。基于卷积神经网络结构建立一个面向高光谱人脸特点的深度网络(VGG12),提取谱带内特征。融合不同谱带的深度特征,利用三层堆栈自编码器(stack auto-encoder,SAE)抽取谱间特征。对提取的谱间和谱内特征,采用最近邻分类器完成最后的识别。结果为了验证提出方法的有效性,在公开的高光谱人脸数据集UWA-HSFD(University of Western Australia hyperspectral face database)和Poly U-HSFD(Hong Kong Polytechnic University hyperspectral face database)上进行对比试验。结果显示,基于分块LBP特征的谱带选择算法优于传统基于整幅图像像素的方法,提出的VGG12网络相比已有深度学习网络,仅保留少量(68个)谱带,在两个数据集上都取得了最高的识别率(96.8%和97.2%),表明传统可见光人脸深度网络并不适合高光谱人脸识别。结论实验结果表明,高光谱数据用于人脸识别中,谱带选择与深度学习结合是有效的,本文方法联合有监督深度网络(VGG12)和无监督学习网络(SAE)挖掘谱内和谱间鉴别特征,在降低深度网络训练复杂度的同时取得了较其他深度网络更好的识别性能。展开更多
In order to combine feature extraction operations with specific hyperspectral remote sensing information processing objectives,two aspects of feature extraction were explored. Based on clustering and decision tree alg...In order to combine feature extraction operations with specific hyperspectral remote sensing information processing objectives,two aspects of feature extraction were explored. Based on clustering and decision tree algorithm,spectral absorption index (SAI),continuum-removal and derivative spectral analysis were employed to discover characterized spectral features of different targets,and decision trees for identifying a specific class and discriminating different classes were generated. By combining support vector machine (SVM) classifier with different feature extraction strategies including principal component analysis (PCA),minimum noise fraction (MNF),grouping PCA,and derivate spectral analysis,the performance of feature extraction approaches in classification was evaluated. The results show that feature extraction by PCA and derivate spectral analysis are effective to OMIS (operational modular imaging spectrometer) image classification using SVM,and SVM outperforms traditional SAM and MLC classifiers for OMIS data.展开更多
文摘目的高光谱人脸数据具有丰富的鉴别信息。最优谱带选择和谱内间特征表示是高光谱人脸识别的关键。基于高光谱波段范围为4001090 nm和采样间隔为10 nm的高光谱成像人脸数据,本文提出一种分块谱带选择和VGG(Visual Geometry Group)网络的高光谱人脸识别方法。方法为了优化适合人脸识别的谱带组合,基于人脸关键点,提出分块局部二值模式(local binary pattern,LBP)特征的Ada Boost支持向量机(support vector machine,SVM)谱带选择方法。基于卷积神经网络结构建立一个面向高光谱人脸特点的深度网络(VGG12),提取谱带内特征。融合不同谱带的深度特征,利用三层堆栈自编码器(stack auto-encoder,SAE)抽取谱间特征。对提取的谱间和谱内特征,采用最近邻分类器完成最后的识别。结果为了验证提出方法的有效性,在公开的高光谱人脸数据集UWA-HSFD(University of Western Australia hyperspectral face database)和Poly U-HSFD(Hong Kong Polytechnic University hyperspectral face database)上进行对比试验。结果显示,基于分块LBP特征的谱带选择算法优于传统基于整幅图像像素的方法,提出的VGG12网络相比已有深度学习网络,仅保留少量(68个)谱带,在两个数据集上都取得了最高的识别率(96.8%和97.2%),表明传统可见光人脸深度网络并不适合高光谱人脸识别。结论实验结果表明,高光谱数据用于人脸识别中,谱带选择与深度学习结合是有效的,本文方法联合有监督深度网络(VGG12)和无监督学习网络(SAE)挖掘谱内和谱间鉴别特征,在降低深度网络训练复杂度的同时取得了较其他深度网络更好的识别性能。
基金Projects 40401038 and 40871195 supported by the National Natural Science Foundation of ChinaNCET-06-0476 by the Program for New Century Excellent Talents in University20070290516 by the Specialized Research Fund for the Doctoral Program of Higher Education
文摘In order to combine feature extraction operations with specific hyperspectral remote sensing information processing objectives,two aspects of feature extraction were explored. Based on clustering and decision tree algorithm,spectral absorption index (SAI),continuum-removal and derivative spectral analysis were employed to discover characterized spectral features of different targets,and decision trees for identifying a specific class and discriminating different classes were generated. By combining support vector machine (SVM) classifier with different feature extraction strategies including principal component analysis (PCA),minimum noise fraction (MNF),grouping PCA,and derivate spectral analysis,the performance of feature extraction approaches in classification was evaluated. The results show that feature extraction by PCA and derivate spectral analysis are effective to OMIS (operational modular imaging spectrometer) image classification using SVM,and SVM outperforms traditional SAM and MLC classifiers for OMIS data.
