虹膜快速检测与精确定位的算法研究

常规虹膜检测算法易受噪声或其他人体特征干扰，针对该缺陷提出一种改进算法，利用动态轮廓模型修正Hough算法得到的估计轮廓。首先，利用Hough算法得到粗略的虹膜估计轮廓，然后借鉴微积分的思想将虹膜图像等分成一定数量的小矩形，引入平滑函数和梯度函数构建动态轮廓模型修正所有小矩形内通过的粗略轮廓，得到精调后的估计轮廓。最后，将该优化算法与Hough算法和传统k means算法进行对比实验，着重在时效性和精确度两个方面进行性能分析。结果表明，改进算法较之传统Hough算法在精确性方面大幅改进，同时在处理图像尺寸为300×200以上的场合下该算法较之k means算法计算效率更高。

Efficient and precise optimization algorithm for iris location

In order to eliminate the flaws in traditional iris detection methods, a new optimization algorithm is proposed, which is based on the theory of the dynamic contour model and used to revise the raw contours computed by Hough circle detection. First, we obtain a raw estimate of the iris contour by Hough algorithm. And then based on the idea of calculus, an iris image is segmented into a number of small rectangles which have the same size. After that we establish a dynamic contour model containing smooth function and gradient function in order to revise raw contours in each rectangle and achieve a precise estimate. Finally, we use the proposed algorithm, Hough algorithm and k means clustering to achieve respective contour estimate in experiment. The result shows that the proposed method have a large advantage over the Hough circle algorithm in terms of precision, and it also have a higher computational efficiency than k means especially in dealing with images which size is over 300×200.