Fusion of Face and Iris Biometrics on Mobile Devices Using Near-infrared Images

With the wide use of cell phones and tablets, large amounts of private data are stored on mobile devices and personal information security has become a growing concern. Biometrics is able to provide encouraging personal recognition solutions to strengthen the security. This paper proposes a multimodal biometric system for mobile devices by fusing face and iris modalities. Face images are aligned according to eye centers and then represented by histograms of Gabor ordinal measures (GOM). Iris images are cropped from face images and represented by ordinal measures (OMs). Finally, the similarity scores produced by face and iris features are combined in the score level. Experiments are conducted on the CASIA-Mobile database which includes 1400 images of 70 Asians. The proposed system achieves impressive results and demonstrates a promising solution for personal recognition on mobile devices.

[1]  Tieniu Tan,et al.  Robust regularized feature selection for iris recognition via linear programming , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[2]  Kang Ryoung Park,et al.  A Study on Iris Localization and Recognition on Mobile Phones , 2008, EURASIP J. Adv. Signal Process..

[3]  Tieniu Tan,et al.  Ordinal Measures for Iris Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Y. Freund,et al.  Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By , 2000 .

[5]  Gil Melfe Mateus Santos,et al.  Fusing iris and periocular information for cross-sensor recognition , 2015, Pattern Recognit. Lett..

[6]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Tieniu Tan,et al.  Gabor Ordinal Measures for Face Recognition , 2014, IEEE Transactions on Information Forensics and Security.

[8]  Michele Nappi,et al.  Mobile Iris Challenge Evaluation (MICHE)-I, biometric iris dataset and protocols , 2015, Pattern Recognit. Lett..

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

[10]  Tieniu Tan,et al.  Toward Accurate and Fast Iris Segmentation for Iris Biometrics , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Michele Nappi,et al.  FIRME: Face and Iris Recognition for Mobile Engagement , 2014, Image Vis. Comput..

[13]  Stefano Ricciardi,et al.  Ubiquitous iris recognition by means of mobile devices , 2015, Pattern Recognit. Lett..

[14]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[15]  Tieniu Tan,et al.  Ordinal Feature Selection for Iris and Palmprint Recognition , 2014, IEEE Transactions on Image Processing.