Representation of local geometry in the visual system
Biological Cybernetics
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
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Affine/ Photometric Invariants for Planar Intensity Patterns
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Monogenic Scale-Space: A Unifying Approach to Phase-Based Image Processing in Scale-Space
Journal of Mathematical Imaging and Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Journal of Cognitive Neuroscience
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
IEEE Transactions on Signal Processing
Gabor-Based Region Covariance Matrices for Face Recognition
IEEE Transactions on Circuits and Systems for Video Technology
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
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In this paper, we present a new face recognition algorithm based on region covariance matrix (RCM) descriptor computed in monogenic scale space. In the proposed model, local energy information and local phase information obtained using monogenic filter is used to represent a pixel at different scales to form region covariance matrix descriptor for each face image during training phase. An eigen-value based distance measure is used to compute the similarity between face images. Extensive experimentation on AT&T and YALE face database has been conducted to reveal the performance of the monogenic scale space based region covariance matrix method and comparative analysis is made with the basic RCM method and Gabor Wavelet based region covariance matrix method to exhibit the superiority of the proposed technique.