Video Iris Recognition Based on Iris Image Quality Evaluation and Semantic Classification

The use of video in biometric applications has reached a great height in the last five years. The iris as one of the most accurate biometric modalities has not been exempt due to the evolution of the capture sensors. In this sense, the use of on line video cameras and the sensors coupled to mobile devices has increased and has led to a boom in applications that use these biometrics as a secure way of authenticating people, some examples are secure banking transactions, access controls and forensic applications, among others. In this work, an approach for video iris recognition is presented. Our proposal is based on a scheme that combines the direct detection of the iris in the video frame with the image quality evaluation and segmentation simultaneously with the video capture process. A measure of image quality is proposed taking into account the parameters defined in ISO /IEC 19794-6 2005. This measure is combined with methods of automatic object detection and semantic image classification by a Fully Convolutional Network. The experiments developed in two benchmark datasets and in an own dataset demonstrate the effectiveness of this proposal.

[1]  Natalia A. Schmid,et al.  Iris Quality Metrics for Adaptive Authentication , 2013, Handbook of Iris Recognition.

[2]  Kiran B. Raja,et al.  Iris imaging in visible spectrum using white LED , 2015, 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[3]  Waleed S.-A. Fathy,et al.  Entropy with Local Binary Patterns for Efficient Iris Liveness Detection , 2017, Wireless Personal Communications.

[4]  Kiran B. Raja,et al.  Smartphone based visible iris recognition using deep sparse filtering , 2015, Pattern Recognit. Lett..

[5]  Zhenan Sun,et al.  Accurate iris segmentation in non-cooperative environments using fully convolutional networks , 2016, 2016 International Conference on Biometrics (ICB).

[6]  Annette Morales-González,et al.  Semantic Segmentation of Color Eye Images for Improving Iris Segmentation , 2017, CIARP.

[7]  Andreas Uhl,et al.  Iris Segmentation Using Fully Convolutional Encoder--Decoder Networks , 2017 .

[8]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[9]  Christoph Busch,et al.  Visible Wavelength Iris Segmentation: A Multi-Class Approach using Fully Convolutional Neuronal Networks , 2018, 2018 International Conference of the Biometrics Special Interest Group (BIOSIG).

[10]  Eduardo Garea-Llano,et al.  image quality evaluation for video iris recognition in the visible spectrum , 2018 .

[11]  John Daugman,et al.  Iris image quality metrics with veto power and nonlinear importance tailoring , 2017 .

[12]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[13]  Shengcai Liao,et al.  Learning Multi-scale Block Local Binary Patterns for Face Recognition , 2007, ICB.

[14]  Babak Nadjar Araabi,et al.  Pigment Melanin: Pattern for Iris Recognition , 2009, IEEE Transactions on Instrumentation and Measurement.

[15]  Alejandro Alvaro Ramírez-Acosta,et al.  Optimized robust multi-sensor scheme for simultaneous video and image iris recognition , 2018, Pattern Recognit. Lett..

[16]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[17]  Patrick J. Flynn,et al.  Iris Recognition Using Signal-Level Fusion of Frames From Video , 2009, IEEE Transactions on Information Forensics and Security.

[18]  Jinyu Zuo,et al.  An Automatic Algorithm for Evaluating the Precision of Iris Segmentation , 2008, 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems.

[19]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.