Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Face recognition from a single image per person: A survey
Pattern Recognition
On solving the face recognition problem with one training sample per subject
Pattern Recognition
Bayes Optimality in Linear Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integration of global and local feature for age estimation of facial images
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Discriminative Multimanifold Analysis for Face Recognition from a Single Training Sample per Person
IEEE Transactions on Pattern Analysis and Machine Intelligence
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For years, researchers in face recognition area have been representing and recognizing faces based on subspace discriminant analysis or statistical learning. Nevertheless, for single sample face recognition these approaches are always suffering from the generalizability problem because of small samples. This paper proposes a novel non-statistics features extraction approach based on fusion of DCT and local Gabor binary pattern Histogram (LGBPH). The global and low frequency features are obtained by low frequency coefficients of discrete cosine transform (DCT). The local and high frequency features are extracted by LGBPH. To integrate the global and local features, the final recognition can be achieved by parallel integration of classification results of the global and local features. In DCT and LGBPH, training procedure is unnecessary to construct the face model, so that the generalizability problem is naturally avoided. The experimental results on ORL face databases show that the global face and local information can be integrated well after level fusion by global and local features, which improve the performance of single sample face recognition.