多模态生物特征识别融合算法的研究
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摘要
多模态生物特征识别技术为当今信息社会中身份识别提供了有效的途径,受到越来越多的研究者的关注。
本文研究了多模态生物特征识别的特征层、分数层融合算法,在开集测试集上测试各种算法的性能,涉及到的生物特征有人脸、掌纹、虹膜三种。
本文的主要研究成果总结如下:
1为了克服典型相关分析(CCA)在信息融合应用中的不足,本文提出了一种有监督的学习方法-增强相关分析(ECA),进而利用核技巧提出了核化的增强相关分析(KECA),并把ECA和KECA用于多模态生物特征的特征层融合。与CCA相比,ECA有效的利用了类别信息,也适用于有样本缺失的情形。开集测试证明,采用ECA、KECA进行特征层融合时,系统的性能(等错误率和正确接收率)较CCA有所提高,且KECA的性能高于ECA。
2设计了一种新颖的近红外人脸、双眼虹膜图像采集设备,构建了国内首个包含近红外人脸、左眼虹膜、右眼虹膜这三种模态的包含噪声样本的多模态数据库。在该库基础上,研究了上述三种模态的融合方法。首先,首次提出了基于联合虹膜编码的双眼虹膜特征层融合算法,其性能高于双眼虹膜分数层融合的性能。其次,采用混合融合结构将三种模态进行融合,即双眼虹膜进行特征层融合后再与近红外人脸进行分数层融合,实验表明其性能高于把三种模态进行分数层融合的方法。
3提出了基于最小二乘法的分数层融合算法(LSMSF)。该算法用最小二乘法估计融合函数的参数,融合函数有三种形式:幂级数函数、多变量多项式函数、简化的多变量多项式函数。采用交叉验证的方法全面评估了LSMSF和传统分数层融合算法的性能,评估包括:单模态性能、分数归一化方法、数据库训练集和测试集的不同划分对算法性能的影响。实验表明,LSMSF的性能均高于传统分数层融合算法,采用多变量多项式函数的LSMSF的性能最好。
Multimodal biometrics provides effective approach for person recognition in the modern world, so it has been paid more and more attentions.
Feature level fusion and score level fusion system are researched in the thesis. Face, iris and palmprint are employed in our research. Experiments are done using the validation and open test set.
The following contributions are made in the thesis.
1. To overcome the shortcomings of CCA application in feature fusion, a novel supervised learning method, termed ad Enhanced Corelation Analysis (ECA) is proposed. With the help of kernel trick, the kernelized ECA (KECA) is further proposed to tackle the linenearly inseperable cases. The class information is employed in the ECA. Also, the ECA can overcome the difficulties due to the loss of samples in real applications. Experiments show that the ECA and KECA outperform other fearure fusion methods and KECA outperforms ECA.
2. A novel high resolution NIR face and irises image device is designed. A noisy multimodal database is founded using the device. Two algorithms are proposed based on the database. 1) A novel joint iris code based both iris feaure fusion algorithm is proposed. Experiments are done using the database founded in this thesis and give promising results. 2) A composed fusion structure is proposed for fusion of face and irises. The feature fusion result of both irises is fused with face in score level.
3. A least squares method based score fusion algorithm (LSMSF) is proposed. The parameters of the fusion function are estimated using the least squares method (LSM). The fusion function could be power series function, multivariate polynomial function and the reduced multivariate polynomial function. Experiments show LSMSF outperforms other score level fusion method under various conditions.
本文研究了多模态生物特征识别的特征层、分数层融合算法,在开集测试集上测试各种算法的性能,涉及到的生物特征有人脸、掌纹、虹膜三种。
本文的主要研究成果总结如下:
1为了克服典型相关分析(CCA)在信息融合应用中的不足,本文提出了一种有监督的学习方法-增强相关分析(ECA),进而利用核技巧提出了核化的增强相关分析(KECA),并把ECA和KECA用于多模态生物特征的特征层融合。与CCA相比,ECA有效的利用了类别信息,也适用于有样本缺失的情形。开集测试证明,采用ECA、KECA进行特征层融合时,系统的性能(等错误率和正确接收率)较CCA有所提高,且KECA的性能高于ECA。
2设计了一种新颖的近红外人脸、双眼虹膜图像采集设备,构建了国内首个包含近红外人脸、左眼虹膜、右眼虹膜这三种模态的包含噪声样本的多模态数据库。在该库基础上,研究了上述三种模态的融合方法。首先,首次提出了基于联合虹膜编码的双眼虹膜特征层融合算法,其性能高于双眼虹膜分数层融合的性能。其次,采用混合融合结构将三种模态进行融合,即双眼虹膜进行特征层融合后再与近红外人脸进行分数层融合,实验表明其性能高于把三种模态进行分数层融合的方法。
3提出了基于最小二乘法的分数层融合算法(LSMSF)。该算法用最小二乘法估计融合函数的参数,融合函数有三种形式:幂级数函数、多变量多项式函数、简化的多变量多项式函数。采用交叉验证的方法全面评估了LSMSF和传统分数层融合算法的性能,评估包括:单模态性能、分数归一化方法、数据库训练集和测试集的不同划分对算法性能的影响。实验表明,LSMSF的性能均高于传统分数层融合算法,采用多变量多项式函数的LSMSF的性能最好。
Multimodal biometrics provides effective approach for person recognition in the modern world, so it has been paid more and more attentions.
Feature level fusion and score level fusion system are researched in the thesis. Face, iris and palmprint are employed in our research. Experiments are done using the validation and open test set.
The following contributions are made in the thesis.
1. To overcome the shortcomings of CCA application in feature fusion, a novel supervised learning method, termed ad Enhanced Corelation Analysis (ECA) is proposed. With the help of kernel trick, the kernelized ECA (KECA) is further proposed to tackle the linenearly inseperable cases. The class information is employed in the ECA. Also, the ECA can overcome the difficulties due to the loss of samples in real applications. Experiments show that the ECA and KECA outperform other fearure fusion methods and KECA outperforms ECA.
2. A novel high resolution NIR face and irises image device is designed. A noisy multimodal database is founded using the device. Two algorithms are proposed based on the database. 1) A novel joint iris code based both iris feaure fusion algorithm is proposed. Experiments are done using the database founded in this thesis and give promising results. 2) A composed fusion structure is proposed for fusion of face and irises. The feature fusion result of both irises is fused with face in score level.
3. A least squares method based score fusion algorithm (LSMSF) is proposed. The parameters of the fusion function are estimated using the least squares method (LSM). The fusion function could be power series function, multivariate polynomial function and the reduced multivariate polynomial function. Experiments show LSMSF outperforms other score level fusion method under various conditions.
引文
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