Exploting periocular and RGB information in fake iris detection

Fake iris detection has been studied by several researchers. However, to date, the experimental setup has been limited to near-infrared (NIR) sensors, which provide grey-scale images. This work makes use of images captured in visible range with color (RGB) information. We employ Gray-Level CoOccurrence textural features and SVM classifiers for the task of fake iris detection. The best features are selected with the Sequential Forward Floating Selection (SFFS) algorithm. To the best of our knowledge, this is the first work evaluating spoofing attack using color iris images in visible range. Our results demonstrate that the use of features from the three color channels clearly outperform the accuracy obtained from the luminance (gray scale) image. Also, the R channel is found to be the best individual channel. Lastly, we analyze the effect of extracting features from selected (eye or periocular) regions only. The best performance is obtained when GLCM features are extracted from the whole image, highlighting that both the iris and the surrounding periocular region are relevant for fake iris detection. An added advantage is that no accurate iris segmentation is needed. This work is relevant due to the increasing prevalence of more relaxed scenarios where iris acquisition using NIR light is unfeasible (e.g. distant acquisition or mobile devices), which are putting high pressure in the development of algorithms capable of working with visible light.

[1]  Julian Fiérrez,et al.  Author's Personal Copy Future Generation Computer Systems a High Performance Fingerprint Liveness Detection Method Based on Quality Related Features , 2022 .

[2]  Sébastien Marcel,et al.  Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition , 2014, IEEE Transactions on Image Processing.

[3]  Julian Fiérrez,et al.  Quality Measures in Biometric Systems , 2012, IEEE Security & Privacy.

[4]  Arun Ross,et al.  Generating Synthetic Irises by Feature Agglomeration , 2006, 2006 International Conference on Image Processing.

[5]  Tieniu Tan,et al.  Counterfeit iris detection based on texture analysis , 2008, 2008 19th International Conference on Pattern Recognition.

[6]  R. M. Haralick,et al.  Textural features for image classification. IEEE Transaction on Systems, Man, and Cybernetics , 1973 .

[7]  John Daugman,et al.  Demodulation by Complex-Valued Wavelets for Stochastic Pattern Recognition , 2003, Int. J. Wavelets Multiresolution Inf. Process..

[8]  Kang Ryoung Park,et al.  Fake Iris Detection by Using Purkinje Image , 2006, ICB.

[9]  Ana F. Sequeira,et al.  MobBIO: A multimodal database captured with a portable handheld device , 2014, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[10]  Patrick J. Flynn,et al.  Image understanding for iris biometrics: A survey , 2008, Comput. Vis. Image Underst..

[11]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[12]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[13]  Fernando Alonso-Fernandez,et al.  Eye detection by complex filtering for periocular recognition , 2014, 2nd International Workshop on Biometrics and Forensics.

[14]  Anil K. Jain,et al.  Biometrics of Next Generation: An Overview , 2010 .

[15]  David A Clausi.  An analysis of co-occurrence texture statistics as a function of grey level quantization , 2002 .

[16]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[17]  Julian Fiérrez,et al.  Direct Attacks Using Fake Images in Iris Verification , 2008, BIOID.

[18]  Patrick J. Flynn,et al.  A Survey of Iris Biometrics Research: 2008-2010 , 2013, Handbook of Iris Recognition.

[19]  Julian Fierrez,et al.  Fingerprint liveness detection based on quality measures , 2009, 2009 First IEEE International Conference on Biometrics, Identity and Security (BIdS).

[20]  Pengfei Shi,et al.  Statistical Texture Analysis-Based Approach for Fake Iris Detection Using Support Vector Machines , 2007, ICB.

[21]  Jaime S. Cardoso,et al.  Iris liveness detection methods in mobile applications , 2014, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[22]  Leen-Kiat Soh,et al.  Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices , 1999, IEEE Trans. Geosci. Remote. Sens..

[23]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[24]  Julian Fiérrez,et al.  Iris liveness detection based on quality related features , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).