Improved Low Resolution Heterogeneous Face Recognition Using Re-ranking

Recently, near-infrared to visible light facial image matching is gaining popularity, especially for low-light and night-time surveillance scenarios. Unlike most of the work in literature, we assume that the near-infrared probe images have low-resolution in addition to uncontrolled pose and expression, which is due to the large distance of the person from the camera. To address this very challenging problem, we propose a re-ranking strategy which takes into account the relation of both the probe and gallery with a set of reference images. This can be used as an add-on to any existing algorithm. We apply it with one recent dictionary learning algorithm which uses alignment of orthogonal dictionaries. We also create a benchmark for this task by evaluating some of the recent algorithms for this experimental protocol. Extensive experiments are conducted on a modified version of the CASIA NIR VIS 2.0 database to show the effectiveness of the proposed re-ranking approach.

[1]  Rama Chellappa,et al.  Synthesis-based Robust Low Resolution Face Recognition , 2017, ArXiv.

[2]  Wanquan Liu,et al.  Low Resolution Face Recognition in Surveillance Systems , 2014 .

[3]  Tieniu Tan,et al.  Transferring deep representation for NIR-VIS heterogeneous face recognition , 2016, 2016 International Conference on Biometrics (ICB).

[4]  Bernhard Schölkopf,et al.  Randomized Nonlinear Component Analysis , 2014, ICML.

[5]  Xianglei Xing,et al.  Couple manifold discriminant analysis with bipartite graph embedding for low-resolution face recognition , 2016, Signal Process..

[6]  Ioannis A. Kakadiaris,et al.  Semi-coupled basis and distance metric learning for cross-domain matching: Application to low-resolution face recognition , 2014, IEEE International Joint Conference on Biometrics.

[7]  Yu-Chiang Frank Wang,et al.  Coupled Dictionary and Feature Space Learning with Applications to Cross-Domain Image Synthesis and Recognition , 2013, 2013 IEEE International Conference on Computer Vision.

[8]  Sivaram Prasad Mudunuri,et al.  Dictionary Alignment for Low-Resolution and Heterogeneous Face Recognition , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[9]  Carlos D. Castillo,et al.  Deep Heterogeneous Feature Fusion for Template-Based Face Recognition , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[10]  Zhenxue Chen,et al.  Low-Resolution Face Recognition of Multi-Scale Blocking CS-LBP and Weighted PCA , 2016, Int. J. Pattern Recognit. Artif. Intell..

[11]  Richa Singh,et al.  Face identification from low resolution near-infrared images , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[12]  Ruimin Hu,et al.  CDMMA: Coupled discriminant multi-manifold analysis for matching low-resolution face images , 2016, Signal Process..

[13]  Tinne Tuytelaars,et al.  Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.

[14]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[15]  Rama Chellappa,et al.  Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[16]  Kwang In Kim,et al.  Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Yu-Chiang Frank Wang,et al.  Recognition at a long distance: Very low resolution face recognition and hallucination , 2015, 2015 International Conference on Biometrics (ICB).

[18]  Thomas S. Huang,et al.  Studying Very Low Resolution Recognition Using Deep Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Pong C. Yuen,et al.  Very low resolution face recognition problem , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[20]  Jiwen Lu,et al.  Learning Compact Binary Face Descriptor for Face Recognition , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Shengcai Liao,et al.  The CASIA NIR-VIS 2.0 Face Database , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[22]  Sébastien Marcel,et al.  Heterogeneous Face Recognition Using Inter-Session Variability Modelling , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[23]  Jakob Verbeek,et al.  Heterogeneous Face Recognition with CNNs , 2016, ECCV Workshops.

[24]  Shiguang Shan,et al.  Multi-View Discriminant Analysis , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Yongjie Chu,et al.  Low-resolution face recognition with single sample per person , 2017, Signal Process..

[26]  M. Saquib Sarfraz,et al.  Deep Perceptual Mapping for Thermal to Visible Face Recogntion , 2015, BMVC.

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

[28]  David W. Jacobs,et al.  Generalized Multiview Analysis: A discriminative latent space , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Narendra Ahuja,et al.  Single image super-resolution from transformed self-exemplars , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Sivaram Prasad Mudunuri,et al.  Low Resolution Face Recognition Across Variations in Pose and Illumination , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Horst Bischof,et al.  Large scale metric learning from equivalence constraints , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Jian-Feng Cai,et al.  Fast Sparsity-Based Orthogonal Dictionary Learning for Image Restoration , 2013, 2013 IEEE International Conference on Computer Vision.

[33]  Quan Pan,et al.  Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  Tieniu Tan,et al.  Learning Invariant Deep Representation for NIR-VIS Face Recognition , 2017, AAAI.

[35]  Nikhil Rasiwasia,et al.  Cluster Canonical Correlation Analysis , 2014, AISTATS.