Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
MutualBoost learning for selecting Gabor features for face recognition
Pattern Recognition Letters
Fusing Gabor and LBP feature sets for kernel-based face recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
InfoBoost for selecting discriminative gabor features
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
AdaBoost gabor fisher classifier for face recognition
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Gabor-Eigen-Whiten-Cosine: a robust scheme for face recognition
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A Comparative Study of Local Matching Approach for Face Recognition
IEEE Transactions on Image Processing
On the results of the first mobile biometry (MOBIO) face and speaker verification evaluation
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Face verification using indirect neighbourhood components analysis
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Cosine similarity metric learning for face verification
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Compact binary patterns (CBP) with multiple patch classifiers for fast and accurate face recognition
CompIMAGE'10 Proceedings of the Second international conference on Computational Modeling of Objects Represented in Images
Face recognition using discriminant sparsity neighborhood preserving embedding
Knowledge-Based Systems
Elliptical local binary patterns for face recognition
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
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One major challenge for face recognition techniques is the difficulty of collecting image samples. More samples usually mean better results but also more effort, time, and thus money. Unfortunately, many current face recognition techniques rely heavily on the large size and representativeness of the training sets, and most methods suffer degraded performance or fail to work if there is only one training sample per person available. This so-called "Single Sample per Person" (SSP) situation is common in face recognition. To resolve this problem, we propose a novel approach based on a combination of Gabor Filter, Local Binary Pattern and Whitened PCA (LGBPWP). The new LGBPWP method has been successfully implemented and evaluated through experiments on 3000+ FERET frontal face images of 1196 subjects. The results show the advantages of our method - it has achieved the best results on the FERET database. The established recognition rates are 98.1%, 98.9%, 83.8% and 81.6% on the fb, fc, dup I, and dup II probes, respectively, using only one training sample per person.