ICB-RW 2016: International challenge on biometric recognition in the wild

Biometric recognition in totally wild conditions, such as the observed in visual surveillance scenarios has not been achieved yet. The ICB-RW competition was promoted to support this endeavor, being the first biometric challenge carried out in data that realistically result from surveillance scenarios. The competition relied on an innovative master-slave surveillance system for the acquisition of face imagery at-a-distance and on-the-move. This paper describes the competition details and reports the performance achieved by the participants algorithms.

[1]  Hugo Proença,et al.  Compensating for pose and illumination in unconstrained periocular biometrics , 2013, Int. J. Biom..

[2]  Erik Learned-Miller,et al.  Labeled Faces in the Wild : Updates and New Reporting Procedures , 2014 .

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

[4]  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.

[5]  Deva Ramanan,et al.  Face detection, pose estimation, and landmark localization in the wild , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Tal Hassner,et al.  Effective face frontalization in unconstrained images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Hugo Proença,et al.  Dynamic camera scheduling for visual surveillance in crowded scenes using Markov random fields , 2015, 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[8]  Shengcai Liao,et al.  Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching , 2012, ACCV.

[9]  James R. Matey,et al.  Iris on the Move: Acquisition of Images for Iris Recognition in Less Constrained Environments , 2006, Proceedings of the IEEE.

[10]  Anil K. Jain,et al.  Face Tracking and Recognition at a Distance: A Coaxial and Concentric PTZ Camera System , 2013, IEEE Transactions on Information Forensics and Security.

[11]  Anil K. Jain,et al.  Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Xiangyu Zhu,et al.  High-fidelity Pose and Expression Normalization for face recognition in the wild , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Anil K. Jain,et al.  A Case Study of Automated Face Recognition: The Boston Marathon Bombings Suspects , 2013, Computer.

[14]  Nicolas Pinto,et al.  Beyond simple features: A large-scale feature search approach to unconstrained face recognition , 2011, Face and Gesture 2011.

[15]  Yi Yao,et al.  Improving long range and high magnification face recognition: Database acquisition, evaluation, and enhancement , 2008, Comput. Vis. Image Underst..

[16]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[17]  Peter H. Tu,et al.  Face recognition at a distance system for surveillance applications , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[18]  Hugo Proença,et al.  Acquiring high-resolution face images in outdoor environments: A master-slave calibration algorithm , 2015, 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).

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