Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Face recognition from a single image per person: A survey
Pattern Recognition
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced local texture feature sets for face recognition under difficult lighting conditions
IEEE Transactions on Image Processing
A regularized correntropy framework for robust pattern recognition
Neural Computation
Maximum Correntropy Criterion for Robust Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation of SIFT features for robust face recognition
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
GA-fisher: a new LDA-based face recognition algorithm with selection of principal components
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face Recognition by Exploring Information Jointly in Space, Scale and Orientation
IEEE Transactions on Image Processing
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Single image based face recognition under different variations such as occlusion, expression and pose has been recognized as an important task in many real-world applications. The popularly widely used holistic features are easily distorted due to occlusion and some other variations. In order to tackle this problem, the sparse local feature descriptor based recognition methods have become more and more important, and promising performance is obtained. The recently developed SIFT, which detects feature points sparsely and extracts feature locally for object matching between different views and scales, can also benefit single image based face recognition. However, we find in this paper that SIFT should not be directly used for face recognition, because face recognition differs from generic object matching. To this end, we develop a new framework for detecting feature keypoints sparsely, describing feature context and matching feature points between two face images. We call this new proposed framework as Facial Sparse Descriptor (FSD). Experiments are conducted to support our analysis of SIFT, and extensive experiments are also presented to validate the proposed FSD against SIFT and its two variants, two dense local feature descriptor (i.e., LBP and HoG), PCA and Gabor based methods on AR, CMU and FERET databases.