Fast periocular authentication in handheld devices with reduced phase intensive local pattern

To ensure highest security in handheld devices, biometric authentication has emerged as a reliable methodology. Deployment of mobile biometric authentication struggles due to computational complexity. For a fast response from a mobile biometric authentication method, it is desired that the feature extraction and matching should take least time. In this article, the periocular region captured through frontal camera of a mobile device is considered under investigation for its suitability to produce a reduced feature that takes least time for feature extraction and matching. A recently developed feature Phase Intensive Local Pattern (PILP) is subjected to reduction giving birth to a feature termed as Reduced PILP (R-PILP), which yields a matching time speed-up of 1.56 times while the vector is 20% reduced without much loss in authentication accuracy. The same is supported by experiment on four publicly available databases. The performance is also compared with one global feature: Phase Intensive Global Pattern, and three local features: Scale Invariant Feature Transform, Speeded-up Robust Features, and PILP. The amount of reduction can be varied with the requirement of the system. The amount of reduction and the performance of the system bears a trade-off. Proposed R-PILP attempts to make periocular suitable for mobile devices.

[1]  Arif Mahmood,et al.  A compact discriminative representation for efficient image-set classification with application to biometric recognition , 2013, 2013 International Conference on Biometrics (ICB).

[2]  Arun Ross,et al.  Handbook of Biometrics , 2007 .

[3]  Luís A. Alexandre,et al.  The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Marios Savvides,et al.  Robust local binary pattern feature sets for periocular biometric identification , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[5]  Karl Ricanek,et al.  LBP-based periocular recognition on challenging face datasets , 2013, EURASIP J. Image Video Process..

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

[7]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[8]  Patrick J. Flynn,et al.  Identifying useful features for recognition in near-infrared periocular images , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[9]  Damon L. Woodard,et al.  Personal identification using periocular skin texture , 2010, SAC '10.

[10]  Damon L. Woodard,et al.  Performance evaluation of local appearance based periocular recognition , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[11]  Ajay Kumar,et al.  Accurate Periocular Recognition Under Less Constrained Environment Using Semantics-Assisted Convolutional Neural Network , 2017, IEEE Transactions on Information Forensics and Security.

[12]  Anil K. Jain,et al.  Periocular biometrics in the visible spectrum: A feasibility study , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[13]  Richa Singh,et al.  Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Damon L. Woodard,et al.  Appearance-based periocular features in the context of face and non-ideal iris recognition , 2011, Signal Image Video Process..

[15]  Luís A. Alexandre,et al.  Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Damon L. Woodard,et al.  Human and Machine Performance on Periocular Biometrics Under Near-Infrared Light and Visible Light , 2012, IEEE Transactions on Information Forensics and Security.

[17]  Damon L. Woodard,et al.  Genetic-Based Type II Feature Extraction for Periocular Biometric Recognition: Less is More , 2010, 2010 20th International Conference on Pattern Recognition.

[18]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[19]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Arun Ross,et al.  On the Fusion of Periocular and Iris Biometrics in Non-ideal Imagery , 2010, 2010 20th International Conference on Pattern Recognition.

[21]  Sambit Bakshi,et al.  Phase Intensive Global Pattern for periocular recognition , 2014, 2014 Annual IEEE India Conference (INDICON).

[22]  Zhaoyang Lu,et al.  Local feature extraction for iris recognition with automatic scale selection , 2008, Image Vis. Comput..

[23]  Luís A. Alexandre,et al.  UBIRIS: A Noisy Iris Image Database , 2005, ICIAP.

[24]  Balasubramanian Raman,et al.  Evaluation of periocular features for kinship verification in the wild , 2017, Comput. Vis. Image Underst..

[25]  Sambit Bakshi,et al.  A novel phase-intensive local pattern for periocular recognition under visible spectrum , 2015 .

[26]  Hugo Proença,et al.  Periocular recognition: Analysis of performance degradation factors , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

[27]  Wei-Yun Yau,et al.  Combining sclera and periocular features for multi-modal identity verification , 2014, Neurocomputing.

[28]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[29]  Kuntal Dey,et al.  Convolutional neural networks for ocular smartphone-based biometrics , 2017, Pattern Recognit. Lett..