Scale-Space for Discrete Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Face recognition: component-based versus global approaches
Computer Vision and Image Understanding - Special issue on Face recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
On the Use of SIFT Features for Face Authentication
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Eigenfeature Regularization and Extraction in Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recent advances in subspace analysis for face recognition
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Face recognition with DAISY descriptors
Proceedings of the 12th ACM workshop on Multimedia and security
Face recognition based on the multi-scale local image structures
Pattern Recognition
A sparse local feature descriptor for robust face recognition
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Privacy preserving picture sharing: enforcing usage control in distributed on-line social networks
Proceedings of the Fifth Workshop on Social Network Systems
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Scale Invariant Feature Transform (SIFT) has shown to be a powerful technique for general object recognition/detection. In this paper, we propose two new approaches: Volume-SIFT (VSIFT) and Partial-Descriptor-SIFT (PDSIFT) for face recognition based on the original SIFT algorithm. We compare holistic approaches: Fisherface (FLDA), the null space approach (NLDA) and Eigenfeature Regularization and Extraction (ERE) with feature based approaches: SIFT and PDSIFT. Experiments on the ORL and AR databases show that the performance of PDSIFT is significantly better than the original SIFT approach. Moreover, PDSIFT can achieve comparable performance as the most successful holistic approach ERE and significantly outperforms FLDA and NLDA.