Iris Location Based on Improved Vector Field Convolution

The iris location is an important task for biometrics systems and which is critical for the iris recognition system. However, typical iris location methods always show the poor performance of huge searching range, noise sensitivity and computational cost. And we have discovered from research in recent years that not every iris image is a regular circle. In previous years, we proposed an precise iris location algorithm based on Vector Field Convolution (VFC) to improve the accuracy of iris location and solve the Non-circular fitting problem. In this work, the four directions Laplacian operator is proposed to settle the problem of location accuracy on the VFC model. The CASIA v1.0, MMU v1.0 and MMU v2.0 iris database are used to verify the accuracy of this improved algorithm. Compared with the traditional iris location algorithm and classical VFC method, this improved VFC snake shows its significant advantages, especially in improving location accuracy.

[1]  Mohit Kumar,et al.  RANSAC lens boundary feature based kernel SVM for transparent contact lens detection , 2019, IET Biom..

[2]  Zexi Li.  An Iris recognition algorithm based on coarse and fine location , 2017, 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(.

[3]  A. Pablo,et al.  On some critical problems for the fractional Laplacian operator , 2011, 1106.6081.

[4]  Emanuele Trucco,et al.  Robust Correspondenceless 3-D Iris Location for Immersive Environments , 2005, ICIAP.

[5]  Baohua Yang,et al.  Iris image quality evaluation method research based on gradation features , 2016, 2016 IEEE International Conference on Signal and Image Processing (ICSIP).

[6]  Junsheng Zheng,et al.  An Iris Recognition Algorithm for Identity Authentication , 2018, 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS).

[7]  David Menotti,et al.  A Benchmark for Iris Location and a Deep Learning Detector Evaluation , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[8]  Bing Li,et al.  Active Contour External Force Using Vector Field Convolution for Image Segmentation , 2007, IEEE Transactions on Image Processing.

[9]  Elhoussaine Ouabida,et al.  Optical approach for iris segmentation and tracking , 2016, 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt).

[10]  Zhilin Liu,et al.  Iris location based on regional property and iterative searching , 2017, 2017 IEEE International Conference on Mechatronics and Automation (ICMA).

[11]  Albana Roci,et al.  Improved segmentation algorithm and further optimization for iris recognition , 2017, IEEE EUROCON 2017 -17th International Conference on Smart Technologies.

[12]  John Daugman,et al.  Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns , 2001, International Journal of Computer Vision.

[13]  B. V. K. Vijaya Kumar,et al.  Application of Correlation Filters for Iris Recognition , 2013, Handbook of Iris Recognition.

[14]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Ashok A. Ghatol,et al.  Iris recognition: an emerging biometric technology , 2007 .

[16]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[17]  Fangfang Yang,et al.  The Iris Feature Point Averaging Method in Student Eye Gaze Tracking , 2018, 2018 37th Chinese Control Conference (CCC).

[18]  Yan Li,et al.  Accurate Iris Location Based on Region of Interest , 2012, 2012 International Conference on Biomedical Engineering and Biotechnology.