The Design and Use of Steerable Filters
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
A Verification Protocol and Statistical Performance Analysis for Face Recognition Algorithms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Boosting Local Feature Based Classifiers for Face Recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
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
Locally linear reconstruction for instance-based learning
Pattern Recognition
AdaBoost Multiple Feature Selection and Combination for Face Recognition
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
An approach to model building for accelerated cooling process using instance-based learning
Expert Systems with Applications: An International Journal
Relative phase in dual tree shearlets
Signal Processing
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In this paper, we propose a novel local steerable phase (LSP) feature extracted from the face image using steerable filters for face recognition. The new type of local feature is semi-invariant under common image deformations and distinctive enough to provide useful identity information. Phase information provided by steerable filters is locally stable with respect to scale changes, noise and brightness changes. Phase features from multiple scales and orientations are concatenated to an augmented feature vector which is used to evaluate similarity between face images. We use a nearest-neighbor classifier based on the local weighted phase-correlation for final classification. The experimental results on FERET dataset show an encouraging recognition performance.