使用opencv比较虹膜图像 [英] Comparing irises images with opencv
问题描述
我需要虹膜比较的帮助。
I need help with iris comparing.
我已经对虹膜图像进行了细分和标准化。现在我想要extraxt功能，将它添加到数据库，或者只是简单地在特征向量列表中，然后将其与其他功能向量进行比较。我希望我的应用程序决定这样的虹膜是否已经存在于数据库中。当然图像是不同的，它们是在不同的光线，角度等处完成的。
I've already segment and normalize my iris images. Now I want to extraxt features, add it to database, or just simply in list of feature vector, and then compare it with other features vector. I want my application to decide if such iris is already in database or not. Of course images are diffenent, they were done in different light, angle etc.
我认为Gabor滤波器会有所帮助，所以我对它有12个不同的参数值：
I thought that Gabor filter would be helpful, so I it to 12 different parameters values:
Mat kernel = Imgproc.getGaborKernel(new Size(25, 25), sigma, theta, lambda, gamma, psi, CvType.CV_64F);
Scalar sum = Core.sumElems(kernel); //kerner normalization
Core.divide(kernel, sum, kernel);
Imgproc.filter2D(floatSource, dest, CvType.CV_64F, kernel);
然后我使用此函数计算12个汉明距离：
Then I compute 12 Hamming distances using this function:
dist_ham = Core.norm(it1.next(), it2.next(), Core.NORM_HAMMING);
获得平均值。
而且......它不起作用。当我比较相同虹膜或2个不同虹膜的2个不同图像时，汉明距离相似。
如何让我的算法更好？也许我应该使用openCV mathers中实现的一些来获得好的结果？对我而言，我将使用哪种算法并不重要，我只想要有好的结果。而且我有点唠叨。
And... it does not work. Hamming distanse is similar when I am comparing 2 different images of the same iris or 2 different irises. How can I make my algorithm better? Maybe I should use some implemented in openCV mathers to obtain good results? For me it don't matter which algorithm I will use, I just want have good results. And I'm a little begginer.
一些示例图片：
第一个人img1：
第1人的标准化虹膜img1：
Some sample pictures: Person one img1: Normalized iris for person one img1:
第一人img2：
第1人img2的标准化虹膜：
Person one img2: Normalized iris for person one img2:
这个例子的汉明距离大约是29000（这是我得到的最低距离，大多数情况下，对于同一个人来说，我得到了大约30000 - 31000）
不同人的汉明距离约为31000（取决于测试图像）
Hamming distance for this example is about 29000 (and this is the lowest distance i got, in the most part i got about 30000 - 31000 for the same person iris) Hamming distance for different persons is about 31000 (depends on tested image)
推荐答案
我在Daugman的论文中实现算法/数学是成功的等我的建议是实际想象gabor内核找到th有意义的参数组合，如sigma和lambda。我没有使用OpenCV的GetGaborKernel，而是使用了手工制作的。
I was successful in doing this just by implementing the algorithm/math in Daugman's paper, etc. My suggestion is to actually visualize the gabor kernels to find the meaningful combination of parameters such as sigma and lambda. I didn't use OpenCV's GetGaborKernel but used a hand-crafted one.
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