Exploring the Usefulness of Light Field Cameras for Biometrics: An Empirical Study on Face and Iris Recognition

A light field sensor can provide useful information in terms of multiple depth (or focus) images, holding additional information that is quite useful for biometric applications. In this paper, we examine the applicability of a light field camera for biometric applications by considering two prominently used biometric characteristics: 1) face and 2) iris. To this extent, we employed a Lytro light field camera to construct two new and relatively large scale databases, for both face and iris biometrics. We then explore the additional information available from different depth images, which are rendered by light field camera, in two different manners: 1) by selecting the best focus image from the set of depth images and 2) combining all the depth images using super-resolution schemes to exploit the supplementary information available within the set elements. Extensive evaluations are carried out on our newly constructed database, demonstrating the significance of using additional information rendered by a light field camera to improve the overall performance of the biometric system.

[1]  Marko Heikkilä,et al.  Description of interest regions with local binary patterns , 2009, Pattern Recognit..

[2]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[3]  Sharath Pankanti,et al.  An evaluation of error confidence interval estimation methods , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[4]  Ramachandra Raghavendra,et al.  Robust Scheme for Iris Presentation Attack Detection Using Multiscale Binarized Statistical Image Features , 2015, IEEE Transactions on Information Forensics and Security.

[5]  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.

[6]  Edward H. Adelson,et al.  Single Lens Stereo with a Plenoptic Camera , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  H.-S. Philip Wong,et al.  A 3D Multi-Aperture Image Sensor Architecture , 2006, IEEE Custom Integrated Circuits Conference 2006.

[8]  Michal Irani,et al.  Improving resolution by image registration , 1991, CVGIP Graph. Model. Image Process..

[9]  Tieniu Tan,et al.  Efficient auto-refocusing of iris images for light-field cameras , 2014, IEEE International Joint Conference on Biometrics.

[10]  Bernadette Dorizzi,et al.  OSIRIS: An open source iris recognition software , 2016, Pattern Recognit. Lett..

[11]  Antonio Torralba,et al.  Contextual Priming for Object Detection , 2003, International Journal of Computer Vision.

[12]  Rama Chellappa,et al.  Handbook of Remote Biometrics , 2009, Advances in Pattern Recognition.

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

[14]  H Stark,et al.  High-resolution image recovery from image-plane arrays, using convex projections. , 1989, Journal of the Optical Society of America. A, Optics and image science.

[15]  P. Hanrahan,et al.  Light Field Photography with a Hand-held Plenoptic Camera , 2005 .

[16]  M. Sujatha,et al.  Recognition of Human Iris Patterns for Biometric Identification , 2015 .

[17]  Dexin Zhang,et al.  Personal Identification Based on Iris Texture Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Kiran B. Raja,et al.  Comparative evaluation of super-resolution techniques for multi-face recognition using light-field camera , 2013, 2013 18th International Conference on Digital Signal Processing (DSP).

[19]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Shmuel Peleg,et al.  Robust super-resolution , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[21]  Kiran B. Raja,et al.  Combining Iris and Periocular Recognition Using Light Field Camera , 2013, 2013 2nd IAPR Asian Conference on Pattern Recognition.

[22]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Andreas Uhl,et al.  Secure Iris Recognition Based on Local Intensity Variations , 2010, ICIAR.

[24]  Philippe Gerigny La Photographie des couleurs , 1924, Nature.

[25]  Zhenan Sun,et al.  Eyelash Removal Using Light Field Camera for Iris Recognition , 2014, CCBR.

[26]  Massimo Tistarelli,et al.  Handbook of Remote Biometrics: for Surveillance and Security , 2009 .

[27]  Faouzi Alaya Cheikh,et al.  Robust iris recognition using light-field camera , 2013, 2013 Colour and Visual Computing Symposium (CVCS).

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

[29]  Ramesh Raskar,et al.  Dappled photography: mask enhanced cameras for heterodyned light fields and coded aperture refocusing , 2007, ACM Trans. Graph..

[30]  E. Adelson,et al.  The Plenoptic Function and the Elements of Early Vision , 1991 .

[31]  R. Gerchberg Super-resolution through Error Energy Reduction , 1974 .

[32]  Kiran B. Raja,et al.  Presentation Attack Detection for Face Recognition Using Light Field Camera , 2015, IEEE Transactions on Image Processing.

[33]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[34]  Kyoil Chung,et al.  A Novel and Efficient Feature Extraction Method for Iris Recognition , 2007 .

[35]  Louis-Philippe Clerc La Photographie des couleurs , 1899 .

[36]  Tieniu Tan,et al.  Light Field Photography for Iris Image Acquisition , 2013, CCBR.

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

[38]  Esa Rahtu,et al.  BSIF: Binarized statistical image features , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[39]  C. Rathgeb,et al.  Context-based texture analysis for secure revocable iris-biometric key generation , 2009, ICDP.

[40]  Bernadette Dorizzi,et al.  The Viterbi algorithm at different resolutions for enhanced iris segmentation , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

[41]  Kiran B. Raja,et al.  A new perspective — Face recognition with light-field camera , 2013, 2013 International Conference on Biometrics (ICB).