IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
On the Use of SIFT Features for Face Authentication
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Journal of Cognitive Neuroscience
Localizing Objects with Smart Dictionaries
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A robust face recognition through statistical learning of local features
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
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This paper proposes a hybrid method for face recognition using local features and statistical feature extraction methods. First, a dense set of local feature points are extracted in order to represent a facial image. Each local feature point is described by the keypoint descriptor defined by SIFT feature. Then, the statistical feature extraction methods, PCA and LDA, are applied to the set of local feature descriptors in order to find low dimensional features. With the obtained low dimensional feature vectors, we can conduct face recognition task efficiently using a simple classifier. Through computational experiments on benchmark data sets, we show that the proposed method is superior to the conventional PCA and LDA in the classification performance. In addition, we also show that the proposed method can achieve remarkable improvement in the processing time compared to the conventional keypoint matching methods proposed for local features.