Face recognition across pose: A review
Pattern Recognition
Iterative subspace analysis based on feature line distance
IEEE Transactions on Image Processing
Uncorrelated discriminant simplex analysis for view-invariant gait signal computing
Pattern Recognition Letters
Probabilistic learning for fully automatic face recognition across pose
Image and Vision Computing
Face virtual pose generation using aligned locally linear regression for face recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Measuring sample distortions in face recognition
Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
Coupled Gaussian process regression for pose-invariant facial expression recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Frontal face generation from multiple low-resolution non-frontal faces for face recognition
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Cross-pose face recognition based on partial least squares
Pattern Recognition Letters
Head pose estimation based on manifold embedding and distance metric learning
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Spatial feature interdependence matrix (SFIM): a robust descriptor for face recognition
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Orthogonal discriminant vector for face recognition across pose
Pattern Recognition
Remote identification of faces: Problems, prospects, and progress
Pattern Recognition Letters
Synthesis of a face image at a desired pose from a given pose
Pattern Recognition Letters
Robust pose invariant face recognition using coupled latent space discriminant analysis
Computer Vision and Image Understanding
Face illumination compensation dictionary
Neurocomputing
Morphable displacement field based image matching for face recognition across pose
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Pose invariant approach for face recognition at distance
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Pose-invariant face recognition in videos for human-machine interaction
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Pose-robust face recognition via sparse representation
Pattern Recognition
Local Linear Regression on Hybrid Eigenfaces for Pose Invariant Face Recognition
International Journal of Computer Vision and Image Processing
Statistical framework for facial pose classification
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Virtual view generation using clustering based local view transition model
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Regularized latent least square regression for cross pose face recognition
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method.