【作者】 张延强；

【导师】 裘正定； 孙冬梅；

【作者基本信息】 北京交通大学 ， 信号与信息处理， 2012， 博士

【摘要】 生物特征识别是采用人所具有的独特的生理或行为特征进行身份鉴别的技术,是解决社会信息化、网络化发展中安全问题的有效方案之一。在部分实际应用中,如法律实施、智能卡应用、身份证和护照等,每类用户仅有一个生物特征样本用于训练,这将导致系统识别性能下降。如何提高单训练样本条件下的生物特征识别性能已经成为生物特征识别领域的研究热点。多模态生物特征融合是一种解决单训练样本生物识别问题的有效途径。本文以手部特征为研究对象,以单训练样本为研究前提,研究了单训练样本条件下的掌纹、指横纹、手指特征识别方法,并采用融合的方法有效地提高了单训练样本条件下手部特征识别系统的性能。本文的主要工作和贡献如下：1.基于子空间分析的掌纹识别研究。分析主成分分析算法(PCA)中各单独主成分的判别性能与对应投影能量的关系,提出一种特征加权的主成分分析方法(FWPCA)。对于单样本掌纹识别,该方法可以采用较少的特征维数得到更佳的识别性能；进一步在FWPCA的基础上增加图像近邻关系矩阵约束,提出一种特征加权的局部保持投影方法(FWLPP),同样取得了识别性能的提升；最后,对PCA的泛化性能进行分析,其本质为特征空间的平移与旋转,当采用大量样本构造特征空间时,对于未知样本可以保持良好的识别性能,有效提高算法的泛化能力。2.提出一种小波子带融合的单样本掌纹识别方法。小波分解能够在保持掌纹图像能量的基础上,有效地降低图像分辨率,并能去除图像中的噪声。然而,小波高频子带因其对噪声和定位误差比较敏感而不能用于掌纹识别。针对这一问题,提出使用均值滤波方法提高掌纹小波水平和垂直子带中主线等几何特征的鲁棒性,使其可以获得与低频子带相当的识别性能,进一步融合小波子带提高单训练样本条件下掌纹识别系统的性能。3.对手指特征进行研究。指横纹是一种较新的手部生物特征,因其缺乏一种有效的定位方法,导致子空间、纹理分析等特征提取方法不可用。提出采用手指刚性区域保证手指的旋转不变性,进而采用多尺度水平方向高通滤波增强指横纹特征,提取能量集中区域从而保证指横纹平移不变性,分别提取指横纹PCA特征、Gabor相位特征、Gabor幅度量化特征用于指横纹识别,匹配分数级融合方法进一步提高了单训练样本指横纹识别系统性能。此外,对包含远指横纹、中指横纹、手形等特征的食指、中指、无名指和小拇指进行单训练样本条件下的识别研究,基于PCA、 LPP特征提取算法表明,这四个手指特征都可用于身份识别,为手部特征融合提供了基础和条件。4.提出一种匹配分数级融合算子评估方法,给出各融合算子判决性能的理论解释。匹配分数级融合算子包括求和、乘积、最大和最小算子,它们是在贝叶斯框架下根据不同假设条件下的理论近似,因其使用简单、无需训练被广泛应用。但在实用过程中,融合算子性能的优劣往往根据实验结果来评价,无法事先估计各算子的性能。提出采用基于概率密度分布的真假两类可分性度量方法评估各融合算子的判决性能。在模态条件独立的前提下,各融合算子的概率密度分布可由单一模态概率密度分布计算出来,根据各融合算子的概率密度分布情况,可以有效评估各融合算子性能,从而实现最优融合算子的自适应选择。5.提出一种基于层次的单样本手部特征识别方法,通过各层匹配阈值的调节,实现不同层次的识别。第一层,中指与小拇指匹配分数融合。若待测样本匹配分数小于设定阈值,则完成识别；否则进入下面的层次进行识别。第二层,食指与无名指匹配分数融合。第三层,掌纹小波子带匹配分数融合。若前三层都未能完成识别,则进入最后一层,即融合前三层匹配分数进行识别。该方法采用由精到粗的匹配策略,使尽可能多的手部特征样本在第一层就完成识别,保证识别精度的同时提高识别效率。由于各层匹配中设定了相应阈值,该手部特征识别算法可以很容易地扩充到生物认证以及开集生物识别系统,有效拓展算法的应用领域。更多还原

【Abstract】 Biometrics is one of the most secure and convenient way to satisfy the requirements for identity digitalization and virtualization in the coming network society, which refers to the automatic identification of an individual by using certain physiological or behavioral traits associated with the person. In some real-world tasks, for example, in the law enforcement scenarios, smart card, passport, only one image per person may be available for training, which may lead to bad recognition result. How to improve the performance of single sample biometrics has become the focus of recent studies. Multimodal biometrics, which utilizes multiple biometric traits, is considered to be efficient alternative for single sample biometrics.This dissertation studies the hand-metrics, which refers to palmprint, kunckleprint, finger features and fusion strategy, to improve the performance of system with single sample for training. First, we present several algorithms for palmprint and finger-based recognition to explore more discriminant features for single sample biometrics. Then, matching score level fusion can greatly improve the performance of single sample biometrics. Finally, a hierarchical recognition strategy based on multiple hand-metrics fusion is proposed, and the evaluation results validate its effectiveness. The main content of this dissertation includes the following aspects:1. Analyze the performances of subspace methods used in palmprint identification, and propose a feature weighted principal component analysis (FWPCA). For PCA based palmprint identification, the system could achieve better performance when removing some principal components. We find the chief reason for such situation is the magnitudes with larger eigenvalues are extraordinarily greater than others, which restrains the abilities of principal components with other eigenvalues. The proposed FWPCA can achieve better performance with lesser features by weighting principal components. In addition, we give a feature weighted locality preserving projections (FWLPP) by adding a graph incorporating neighborhood structure of the palmprint space. The experimental results demonstrate the effectiveness of FWPCA and FWLPP. Finally, analyze the generalization capability of PCA for palmprint recognition, and find it can be improved by using enough samples for training. 2. A wavelet sub-bands fusion scheme for single sample palmprint recognition is proposed. Wavelet decomposition has been applied in palmprint recognition successfully. However, only the low frequency sub-band was used for further feature extraction, while the high frequency sub-bands were considered to be unsuitable for palmprint recognition due to their sensitivity to noise and shape distortion. We utilize mean filtering to enhance the robustness of the high frequency sub-bands. Experimental results show that the performances of the horizontal and vertical high frequency sub-bands can be promoted up to a competitive level, and the fusion scheme, which combines the matching scores of high frequency sub-bands with that of low frequency sub-band, improves the performance of single sample palmprint recognition greatly, and is superior to the conventional recognition methods.3. Personal identification algorithms based on finger features are explored. A new location algorithm of inner knuckleprint is proposed. The rigid deformation property of finger is used to keep rotation invariance. High-pass filter with horizontally is used to enhance the line features in finger image. Radon projection verifies the translational invariance, and the location of inner knuckleprint in original finger image is accurately located. Subspace and texture analysis are utilized for features extraction, and matching score fusion can further extend the recognition performance. Furthermore, finger-based biometrics, which includes inner knuckleprint and finger shape features, would give more discriminant information. We extract PCA and LPP features based on index finger, middle finger, ring finger and little finger. The experimental results demonstrate the effectiveness of finger-based biometrics in recognition accuracy even with single sample for training. They provide the basis for hand-metrics fusion.4. An evaluation method of fusion operator for matching score fusion is proposed. It can provide a theoretical support to evaluate the performance of fusion operators. Fusion operator, which contains sum rule, product rule, max rule and min rule, is common and simple scheme for information fusion and is considered as special case of compound classification based on the Bayesian framework. They are usually used and validated in most multimodal biometric systems, but the optimal operator is obtained experimentally in real-world systems. The proposed method for optimal fusion operator selection by first estimating the probability density function (PDF) of each feature score and then calculating the PDFs of fusion operators on the assumption that the representations used are conditionally statistically independent. The distance between the class of genuine and impostor, which is based on the theory of probability density distribution, can be used to evaluate the capability of fusion operators. The results of40experiments based on Hand database validate the proposed method.5. A hierarchical hand-metrics identification scheme is proposed for single sample biometrics. At the first level, the matching scores of middle finger and little finger features are fused for recognition. Most of individuals are recognized accurately, and few of them will fall into following levels. At the second level, the matching scores of index finger and ring finger features are fused for recognition. At the third level, the matching scores of palmprint wavelet sub-bands features are fused for recognition. If some individuals can not be recognized, they will fall into the fourth level, and all above scores will be fused for final identification. The experimental results demonstrate the proposed hierarchical identification scheme could perform almost perfectly for single sample biometrics under laboratory conditions.更多还原