Introduction: Computer Vision Research at NECI
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Line Edge Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition under Varying Views
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Face Recognition Based on Nearest Linear Combinations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Generating Discriminating Cartoon Faces Using Interacting Snakes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Recent advances in visual and infrared face recognition: a review
Computer Vision and Image Understanding
Three-Dimensional Face Recognition
International Journal of Computer Vision
Multiscale Fusion of Visible and Thermal IR Images for Illumination-Invariant Face Recognition
International Journal of Computer Vision
An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of face images using local iterated function systems
Machine Vision and Applications
Artificial Neural Network Based Automatic Face Model Generation System from Only One Fingerprint
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Feature based RDWT watermarking for multimodal biometric system
Image and Vision Computing
Biometrics and their use in e-passports
Image and Vision Computing
Face recognition with disguise and single gallery images
Image and Vision Computing
Linguistics and face recognition
Journal of Visual Languages and Computing
Robust visual similarity retrieval in single model face databases
Pattern Recognition
An intelligent automatic face contour prediction system
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Enhanced human face image searching system using relevance feedback
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face recognition using local graph structure (LGS)
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: interaction techniques and environments - Volume Part II
Fusion of locally linear embedding and principal component analysis for face recognition (FLLEPCA)
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
An improved hybrid approach to face recognition by fusing local and global discriminant features
International Journal of Biometrics
Overlapping local phase feature (OLPF) for robust face recognition in surveillance
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Facilitating fashion camouflage art
Proceedings of the 21st ACM international conference on Multimedia
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We consider the problem of feature-based face recognition in the setting where only a single example of each face is available for training. The mixture-distance technique we introduce achieves a recognition rate of 95% on a database of 685 people in which each face is represented by 30 measured distances. This is currently the best recorded recognition rate for a feature-based system applied to a database of this size. By comparison, nearest neighbor search using Euclidean distance yields 84%. In our work a novel distance function is constructed based on local second order statistics as estimated by modeling the training data as a mixture of normal densities. We report on the results from mixtures of several sizes. We demonstrate that a flat mixture of mixtures performs as well as the best model and therefore represents an effective solution to the model selection problem. A mixture perspective is also taken for individual Gaussians to choose between first order (variance) and second order (covariance) models. Here an approximation to flat combination is proposed and seen to perform well in practice. Our results demonstrate that even in the absence of multiple training examples for each class, it is sometimes possible to infer from a statistical model of training data, a significantly improved distance function for use in pattern recognition.