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跨设备虹膜图像的鲁棒识别
其他题名Robust Recognition of Cross-sensor Iris Images
校利虎
2014-11-29
学位类型工学博士
中文摘要在移动互联网和大数据技术飞速发展的背景下,越来越多的科研机构和公司投身于虹膜识别的研究。而虹膜图像获取装置的规模化发展,为虹膜识别系统的推广提出了新的挑战。本文以对跨设备虹膜图像识别技术的研究为主线,从传统的虹膜识别的流程出发,采用“分而治之,各个突破”的策略从图像特征抽取、特征选择、特征映射和信息融合等关键环节开展我们的研究工作。通过采用图像处理和模式识别的相关方法消除外在环境如成像器件和成像环境等因素对虹膜图像本质特性的影响,力图实现快速、稳定和准确的跨设备虹膜识别系统。本文的主要工作和创新点如下: 1. 提出了跨设备虹膜图像识别的鲁棒特征表达方法。通过分析现有特征表达方法的局限,提出了多方向加权编码的特征表达方法。该方法能够得到鲁棒的虹膜图像特征。通过借鉴局部描述统计子的编码策略,将多方向加权编码策略拓展到眼周的鲁棒图像特征提取。同时提出了一种基于设备属性的虹膜纹理和眼周纹理的融合策略。 2. 提出基于关联结构稀疏约束的跨设备虹膜图像的特征选择方法。该方法将跨设备虹膜识别问题转化为一种基于l21约束的关联特征选择问题。由于该优化模型近于非凸难于直接求解,因此提出了基于半二次优化的迭代优化算法。该方法能够在有限的迭代次数下得到更优的结果。在此基础上对该方法进行拓展,引入了关联空间的相似度约束,提高所选特征的表达性能。 3. 提出了一种基于最大间隔线性规划的跨设备虹膜图像的特征选择方法。该方法通过引入的关联特征空间的约束,可以较好的建立起采集于不同设备的虹膜图像的特征空间的相关关系,同时引入非负稀疏约束以实现跨设备虹膜图像的特征选择,尽可能的减小了跨设备因素的影响。为了有效的对该模型进行求解,将关联特征空间的约束转化为两个不等式约束,从而将原问题转化为标准的有不等式约束的的线性规划问题,从而可以采用标准的线性优化工具包进行有效快速求解,大大降低了问题的复杂度。 4. 提出了跨设备虹膜图像的特征映射方法。在做完特征选择之后,通过引入线性嵌入核函数,将选择到的特征子集映射到共同的高维空间,再进行模式分类,减小了跨设备虹膜图像的特征层面的差异性。
英文摘要With the increasing attention of public security and rapidly development of mobile internet technology, a number of different iris recognition systems are widely deployed in the real world due to the reliability of iris iometrics, which poses new challenges to iris recognition algorithms. In this paper, by adopting the strategy of “divide and conquer”, we focus on cross sensor iris recognition, and start with the traditional procedures, feature extraction, feature selection, feature mapping and fusion with periocular region. By using technologies of image processing and pattern recognition, factors of imaging sensors and environments will be diminished or eliminated,which is of great help to the deeper research and large-scale applications of iris recognition systems. The main contributions are as follows : 1. A robust feature extraction method for cross-sensor iris recognition is proposed. By analyzing the limitations of existing feature extraction methods, we propose a multi-direction weighting ordinal features(MultiOM) which can obtain robust iris features with better discriminative abilities. We also extend this feature extraction method to periocular feature extraction. In addition, a novel fusion strategy of iris texture and periocular texture is proposed. 2. We propose the coupled feature selection method for cross-sensor iris recognition. This cross-sensor matching problem is transformed to a framework of feature selection method based on l21-norm. However, this framework is difficult to solve because of the non-convex problem. Based on half-quadratic optimization, an iterative optimization algorithm is developed to obtain the global optimum. In addition, a similarity measure constraint is introduced to this framework, so as to select the more powerful features for cross-sensor iris recognition. 3. A margin based feature selection method for cross-sensor iris recognition via linear programming is proposed. To take advantage of the large margin principle and by means of novel constraints, relationship between coupled feature spaces is established. We reformulated the constraints in terms of linear inequalities. Then this model can be computed conveniently via the Simplex algorithm. 4. A feature mapping method is proposed for cross-sensor iris recognition. After the step of feature selection, these selected features from coupled feature spaces will be transformed to high-dimension subspace with better discriminative ability and smaller differ...
关键词虹膜识别 跨设备 异质识别 特征选择 信息融合 特征抽取 特征映射 Iris Recognition Cross Sensor Heterogenous Images Feature Selection Feature Extraction Feature Mapping Information Fusion
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6661
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
校利虎. 跨设备虹膜图像的鲁棒识别[D]. 中国科学院自动化研究所. 中国科学院大学,2014.
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