Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
What is the set of images of an object under all possible lighting conditions?
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face Recognition by Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Appearance-Based Face Recognition and Light-Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Modal Tensor Face for Simultaneous Super-Resolution and Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Total Variation Models for Variable Lighting Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Computer Vision and Image Understanding
Tied Factor Analysis for Face Recognition across Large Pose Differences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Estimation of Albedo for Illumination-Invariant Matching and Shape Recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition across pose: A review
Pattern Recognition
Using Stereo Matching with General Epipolar Geometry for 2D Face Recognition across Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Coupled Metric Learning for Face Recognition with Degraded Images
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Face recognition under varying illumination using gradientfaces
IEEE Transactions on Image Processing
Distant face recognition based on sparse-stereo reconstruction
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Facial Deblur Inference Using Subspace Analysis for Recognition of Blurred Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition under variable lighting using harmonic image exemplars
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Energy Normalization for Pose-Invariant Face Recognition Based on MRF Model Image Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition from video using the generic shape-illumination manifold
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Eigenface-domain super-resolution for face recognition
IEEE Transactions on Image Processing
Locally Linear Regression for Pose-Invariant Face Recognition
IEEE Transactions on Image Processing
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Example-Driven Manifold Priors for Image Deconvolution
IEEE Transactions on Image Processing
Synthesis-based recognition of low resolution faces
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
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Face recognition in unconstrained acquisition conditions is one of the most challenging problems that has been actively researched in recent years. It is well known that many state-of-the-art still face recognition algorithms perform well, when constrained (frontal, well illuminated, high-resolution, sharp, and full) face images are acquired. However, their performance degrades significantly when the test images contain variations that are not present in the training images. In this paper, we highlight some of the key issues in remote face recognition. We define the remote face recognition as one where faces are several tens of meters (10-250m) from the cameras. We then describe a remote face database which has been acquired in an unconstrained outdoor maritime environment. Recognition performance of a subset of existing still image-based face recognition algorithms is evaluated on the remote face data set. Further, we define the remote re-identification problem as matching a subject at one location with candidate sets acquired at a different location and over time in remote conditions. We provide preliminary experimental results on remote re-identification. It is demonstrated that in addition to applying a good classification algorithm, finding features that are robust to variations mentioned above and developing statistical models which can account for these variations are very important for remote face recognition.