Pose-invariant features and personalized correspondence learning for face recognition

[1]  2018 International Conference on Audio, Language and Image Processing (ICALIP) , 2018 .

[2]  Hyo Jong Lee,et al.  Cross-pose face recognition based on multiple virtual views and alignment error , 2015, Pattern Recognit. Lett..

[3]  Yandong Hou,et al.  Sparse representation-based robust face recognition by graph regularized low-rank sparse representation recovery , 2015, Neurocomputing.

[4]  Jiwen Lu,et al.  Single Sample Face Recognition via Learning Deep Supervised Autoencoders , 2015, IEEE Transactions on Information Forensics and Security.

[5]  Gang Wang,et al.  Joint Feature Learning for Face Recognition , 2015, IEEE Transactions on Information Forensics and Security.

[6]  Xin Liu,et al.  Maximal Likelihood Correspondence Estimation for Face Recognition Across Pose , 2014, IEEE Transactions on Image Processing.

[7]  Hyo Jong Lee,et al.  Pose unconstrained face recognition based on SIFT and alignment error , 2014, 2014 International Conference on Audio, Language and Image Processing.

[8]  Shiguang Shan,et al.  Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Jian Sun,et al.  Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Matti Pietikäinen,et al.  Multiscale Local Phase Quantization for Robust Component-Based Face Recognition Using Kernel Fusion of Multiple Descriptors , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Rama Chellappa,et al.  Pose-Invariant Face Recognition Using Markov Random Fields , 2013, IEEE Transactions on Image Processing.

[13]  Simon C. K. Shiu,et al.  Robust Kernel Representation With Statistical Local Features for Face Recognition , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Jonghyun Choi,et al.  Robust pose invariant face recognition using coupled latent space discriminant analysis , 2012, Comput. Vis. Image Underst..

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

[16]  Xin Liu,et al.  Morphable Displacement Field Based Image Matching for Face Recognition across Pose , 2012, ECCV.

[17]  Michael J. Jones,et al.  Fully automatic pose-invariant face recognition via 3D pose normalization , 2011, 2011 International Conference on Computer Vision.

[18]  Tal Hassner,et al.  Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Marios Savvides,et al.  Unconstrained Pose-Invariant Face Recognition Using 3D Generic Elastic Models , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Josef Kittler,et al.  Energy Normalization for Pose-Invariant Face Recognition Based on MRF Model Image Matching , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  S. Shan,et al.  Maximizing intra-individual correlations for face recognition across pose differences , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Jean-Michel Morel,et al.  ASIFT: A New Framework for Fully Affine Invariant Image Comparison , 2009, SIAM J. Imaging Sci..

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

[24]  Tsuhan Chen,et al.  Learning patch correspondences for improved viewpoint invariant face recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Jonathan Warrell,et al.  Tied Factor Analysis for Face Recognition across Large Pose Differences , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Wen Gao,et al.  Locally Linear Regression for Pose-Invariant Face Recognition , 2007, IEEE Transactions on Image Processing.

[27]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[29]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[30]  Wen Gao,et al.  Efficient 3D reconstruction for face recognition , 2005, Pattern Recognit..

[31]  C. Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[32]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[33]  Thomas Vetter,et al.  Face Recognition Based on Fitting a 3D Morphable Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  S. C. Hui,et al.  Fast face identification under varying pose from a single 2-D model view , 2001 .

[35]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[36]  T. Poggio,et al.  MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES , 2001 .

[37]  Wen Gao,et al.  Coupled Bias–Variance Tradeoff for Cross-Pose Face Recognition , 2012, IEEE Transactions on Image Processing.

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