The Design and Use of Steerable Filters
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
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Face Recognition Based on the Appearance of Local Regions
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Random sampling LDA for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Null space-based kernel fisher discriminant analysis for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Video-rate stereo depth measurement on programmable hardware
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Design of steerable filters for feature detection using canny-like criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
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Both local features and holistic features are critical for face recognition and have different contributions. In this paper, we first propose a novel local steerable feature extracted from the face image using steerable filter for face representation. Discriminant information provided by steerable filter is locally stable with respect to scale, noise and brightness changes and it is semi-invariant under common image deformations and distinctive enough to provide useful identity information. We then present a new null space method based on random subspace. Linear Discriminant Analysis (LDA) is a popular holistic feature extraction technique for face recognition. Null Space LDA (NLDA) and Fisherface are adopted to extract global feature in the steerable feature space. Based on random subspaces, multiple NLDA classifiers are constructed under the most suitable situation for the null space. NLDA takes full advantage of the null space, while Fisherface extracts the most discriminant information in the principal subspace. Fisherface classifiers are constructed from the same set of random subspaces for NLDA classifiers. In each random subspace, Fisherface and NLDA share a unique eigen-analysis. There is no redundancy between such two kinds of complementary classifiers. Finally, all of the classifiers are integrated using a fusion rule. Experimental results on different face data sets demonstrate the effectiveness of the proposed method.