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基于微积分算子的彩色虹膜图像定位算法 被引量:2

Location Algorithm of RGB Iris Image Based on Integro-Differential Operators
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摘要 针对NICE:Ⅱ中的彩色噪声虹膜图像难于精确定位问题,提出了一种基于微积分算子的彩色虹膜图像定位算法.首先选择RGB虹膜图像的R层,结合Gabor滤波器和图像梯度信息检测图像中的光斑区域;然后利用Adaboost算法检测虹膜区域,并使用抛物线形微积分算子定位上下眼睑;再利用基于微积分算子的模板,通过局部极值的逐步迭代和对虹膜边界点的聚类分析,定位虹膜外边界;最后使用同态滤波对虹膜区域进行增强处理,检测虹膜内边界.在NICE:Ⅱ彩色虹膜图像库上的实验表明,该方法的定位准确率为98.22%. A location algorithm based on integro-differential operators was presented for solving the difficulty of precise segmentation in NICE:Ⅱ.The R layer of RGB iris image is chosen first,and the Gabor filter and gradient information are used to detect the reflection area.Moreover,Adaboost algorithm is used to detect iris area in the whole image,then the upper and lower eyelids are detected.A center template based on integro-differential operators was adopted to search the outer boundary of iris through iteration step by step,and cluster analysis was used to remove the noise on iris boundaries.Because the inner boundary of iris is weak,the homomorphic filtering is adopted to enhance the iris area,then the inner boundary is located.The proposed algorithm was tested under NICE:Ⅱ,and the accuracy of the new method is 98.22%.
机构地区 东北大学理学院
出处 《东北大学学报(自然科学版)》 EI CAS CSCD 北大核心 2011年第11期1550-1553,共4页 Journal of Northeastern University(Natural Science)
基金 国家自然科学基金资助项目(10801026)
关键词 彩色虹膜图像 微积分算子 虹膜定位 光斑检测 ADABOOST算法 RGB iris image integro-differential operator iris location reflection area detection Adaboost algorithm
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  • 1Tan T N, He Z F, Sun Z N. Efficient and robust segmentation of noisy iris images for non-cooperative iris reeognition[J ]. Image and Vision Computing, 2010, 28 (2) :223-230.
  • 2Sankowski W, Grabowski K, Zubert M, et al. Reliable algorithm for iris segmentation in eye image[J ]. Image and Vision Computing, 2009,26(2):231-237.
  • 3Chen Y, Adjouadi M, Han C G, et al. A highly accurate and computationally efficient approach for unconstrained iris segmentation[J]. Image and Vision Computing, 2009,26 (2) :261-269.
  • 4Li P H, Liu X M, Xiao L J, et al. Robust and accurate iris segmentation in very noisy iris images[J]. Image and Vision Computing, 2010,28(2):246 -253.
  • 5Dong W B, Sun Z N, Tan T N, et al. Self-adaptive iris image acquisition system [ C]//Proceedings of the SPIE, Biometric Technology for Human Identification. Orlando, 2008:6-14.
  • 6He Z F, Tan T N, Sun Z N, et al. Towards accurate and fast iris segmentation for iris biometrics [J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009,31 (9) : 1670-1684.
  • 7Zhang X D, Wang Q, Zhu H G, et al. Noise detection of iris image based on texture analysis [ C] //2009 Chinese Control and Decision Conference. Guilin, 2009 : 311-315.
  • 8Daugman J G. High confidence visual recognition of persons by a test of statistical independence[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15 (11) :1148-1161.
  • 9Wildes A. Iris recognition: an emerging biometric technology [J]. Proceedings of the IEEE, 1997,85(9) :1348-1363.
  • 10Jeong D S, Hwang J W, Kang B J, et al. A new iris segmentation method for non-ideal iris images [ J ]. Image and Vision Computing, 2010,28 (2) : 254-260.

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