Trends and Controversies

Performing covert biometric recognition in surveillance environments has been regarded as a grand challenge, considering the adversity of the conditions where recognition should be carried out (e.g., poor resolution, bad lighting, off-pose and partially occluded data). This special issue compiles a group of approaches to this problem.

[1]  Avtor Vitomir Štruc.  1 Performance Evaluation of Photometric Normalization Techniques for Illumination Invariant Face Recognition , 2011 .

[2]  Andrew Zisserman,et al.  Fisher Vector Faces in the Wild , 2013, BMVC.

[3]  Hugo Proença,et al.  Quis-Campi: Extending in the Wild Biometric Recognition to Surveillance Environments , 2015, ICIAP Workshops.

[4]  Xiangyu Zhu,et al.  Face Alignment in Full Pose Range: A 3D Total Solution , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Junjie Yan,et al.  Learn to Combine Multiple Hypotheses for Accurate Face Alignment , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[6]  Bruce A. Draper,et al.  The CSU Face Identification Evaluation System: Its Purpose, Features, and Structure , 2003, ICVS.

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

[8]  Anil K. Jain,et al.  Component-Based Representation in Automated Face Recognition , 2013, IEEE Transactions on Information Forensics and Security.

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

[10]  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.

[11]  Alice J. O'Toole,et al.  Comparison of human and computer performance across face recognition experiments , 2014, Image and Vision Computing.

[12]  Rainer Stiefelhagen,et al.  Combining view-based pose normalization and feature transform for cross-pose face recognition , 2015, 2015 International Conference on Biometrics (ICB).

[13]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[14]  Shengcai Liao,et al.  Learning Face Representation from Scratch , 2014, ArXiv.

[15]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Mislav Grgic,et al.  SCface – surveillance cameras face database , 2011, Multimedia Tools and Applications.

[17]  Julian Fiérrez,et al.  Dealing with occlusions in face recognition by region-based fusion , 2016, 2016 IEEE International Carnahan Conference on Security Technology (ICCST).

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

[19]  Yann LeCun,et al.  Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[20]  Biao Wang,et al.  Illumination Normalization Based on Weber's Law With Application to Face Recognition , 2011, IEEE Signal Processing Letters.

[21]  Julian Fierrez,et al.  Combination of Face Regions in Forensic Scenarios , 2015, Journal of forensic sciences.