基于感兴趣区的虹膜定位算法

快速虹膜定位是实现虹膜自动识别系统的基础.利用小波变换后LL频带是图像内容的抽样缩略的性质,只对LL频带的图像进行虹膜定位,基于图像光斑特性选择定位感兴趣区域,用Hough变换定位虹膜内圆,单行梯度最大值方法定位虹膜外圆,然后按比例还原所有的参数值.试验结果表明这些参数与在原始图像中进行虹膜定位得到的参数一致,定位时间大大缩短,说明了该定位算法的有效性.     

Iris segmentation in non-ideal images using graph cuts

A non-ideal iris image segmentation approach based on graph cuts is presented that uses both the appearance and eye geometry information. A texture measure based on gradients is computed to discriminate between eyelash and non-eyelash regions, combined with image intensity differences between the iris, pupil, and the background (region surrounding the iris) are utilized as cues for segmentation. The texture and intensity distributions for the various regions are learned from histogramming and explicit sampling of the pixels estimated to belong to the corresponding regions. The image is modeled as a Markov Random Field and the energy minimization is achieved via graph cuts to assign each image pixel one of the four possible labels: iris, pupil, background, and eyelash. Furthermore, the iris region is modeled as an ellipse, and the best fitting ellipse to the initial pixel based iris segmentation is computed to further refine the segmented region. As a result, the iris region mask and the parameterized iris shape form the outputs of the proposed approach that allow subsequent iris recognition steps to be performed for the segmented irises. The algorithm is unsupervised and can deal with non-ideality in the iris images due to out-of-plane rotation of the eye, iris occlusion by the eyelids and the eyelashes, multi-modal iris grayscale intensity distribution, and various illumination effects. The proposed segmentation approach is tested on several publicly available non-ideal near infra red (NIR) iris image databases. We compare both the segmentation error and the resulting recognition error with several leading techniques, demonstrating significantly improved results with the proposed technique.     

Iris recognition: Analysis of the error rates regarding the accuracy of the segmentation stage

Iris recognition has been widely used in several scenarios with very satisfactory results. As it is one of the earliest stages, the image segmentation is in the basis of the process and plays a crucial role in the success of the recognition task. In this paper we analyze the relationship between the accuracy of the iris segmentation process and the error rates of three typical iris recognition methods. We selected 5000 images of the UBIRIS, CASIA and ICE databases that the used segmentation algorithm can accurately segment and artificially simulated four types of segmentation inaccuracies. The obtained results allowed us to conclude about a strong relationship between translational segmentation inaccuracies – that lead to errors in phase – and the recognition error rates.     

Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs

We present algorithms for iris segmentation, feature extraction and selection, and iris pattern matching. To segment the inner boundary from a nonideal iris image, we apply a level set based curve evolution approach using the edge stopping function, and to detect the outer boundary, we employ the curve evolution approach using the regularized Mumford-Shah segmentation model with an energy minimization algorithm. Daubechies wavelet transform (DBWT) is used to extract the textural features, and genetic algorithms (GAs) are deployed to select the subset of informative features by combining the valuable outcomes from the multiple feature selection criteria without compromising the recognition accuracy. To speed up the matching process and to control the misclassification error, we apply a combined approach called the adaptive asymmetrical support vector machines (AASVMs). The parameter values of SVMs are also optimized in order to improve the overall generalization performance. The verification and identification performance of the proposed scheme is validated using the UBIRIS Version 2, the ICE 2005, and the WVU datasets.     

虹膜定位

为了提高虹膜定位的速度以及虹膜定位算法的健壮性,提出了一种粗定位与精定位相结合的两步定位法,用以进行虹膜定位,并对现有的虹膜定位算法进行了一些改进,实验结果表明,用两步法进行虹膜定位可以加快定位速度,减少搜索计算的盲目性。     

基于数学形态学的虹膜定位算法

针对现有的虹膜定位算法的局限性,提出了一种基于数学形态学的虹膜定位算法。内圆定位是利用二值图像形态学的方法提取瞳孔的圆心和半径。外圆的定位用形态学进行边缘提取与Hough变换相结合,确定外圆圆心及半径。实验证明了该算法的合理性和有效性。     

一种基于小波过零检测和SVM的虹膜识别方法*

提出了一种新的基于SVM的虹膜识别算法,通过对虹膜纹理采用小波变换来实现特征提取,最后通过SVM完成模式匹配。实验结果表明,该算法识别率高并可有效地应用于虹膜身份鉴别系统中。     

一种新的基于小波过零检测的虹膜识别算法*

虹膜识别被认为是目前最准确可靠的生物特征识别方法。提出了一种新的基于小波过零检测的虹膜识别算法,通过对分离的虹膜纹理采用小波变换来实现特征提取,最后通过Hamming距离完成模式匹配。实验结果表明,该算法识别率很高,可有效地应用于身份鉴别系统中。     

Iris detection using intensity and edge information

In this paper we propose a new algorithm to detect the irises of both eyes from a face image. The algorithm first detects the face region in the image and then extracts intensity valleys from the face region. Next, the algorithm extracts iris candidates from the valleys using the feature template of Lin and Wu (IEEE Trans. Image Process. 8 (6) (1999) 834) and the separability filter of Fukui and Yamaguchi (Trans. IEICE Japan J80-D-II (8) (1997) 2170). Finally, using the costs for pairs of iris candidates proposed in this paper, the algorithm selects a pair of iris candidates corresponding to the irises. The costs are computed by using Hough transform, separability filter and template matching. As the results of the experiments, the iris detection rate of the proposed algorithm was 95.3% for 150 face images of 15 persons without spectacles in the database of University of Bern and 96.8% for 63 images of 21 persons without spectacles in the AR database.     

虹膜图象质量评价的研究

一幅虹膜图象采集后 ,首先要进行质量评价 ,再根据评价结果确定其是否可以用于虹膜识别 .将虹膜图象质量评价分为 3个部分 :虹膜检测、虹膜不可用区域的确定以及虹膜图象清晰度评价 ,即采用模板匹配法在图象中检测可能的虹膜 ,并利用虹膜图象的一些特征来校验其可信度 ;寻找虹膜与眼皮的边界线 ,计算虹膜被眼皮遮盖的面积 ,同时根据虹膜和瞳孔的边界参数来计算位于图象外虹膜面积的大小 ,以确定不可用虹膜部分的大小 ;用灰度差分累加和的方法计算瞳孔边沿的平均高度 ,以判断图象中虹膜的清晰程度 .实验结果表明 ,该方法能够有效地筛选出符合要求的虹膜图象 .