Free form face recognition using kernel sparse representation

Authors:
K. R. Anoop;R. S. Narasimhan;K. R. Ramakrishnan;Chiranjib Bhattacharya
Affiliations:
Indian Institute of Science, Bangalore, India;Indian Institute of Science, Bangalore, India;Indian Institute of Science, Bangalore, India;Indian Institute of Science, Bangalore, India
Venue:
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Year:
2010

Citing 14
Cited 0

Texture Features for Browsing and Retrieval of Image Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose

IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Robust Real-Time Face Detection

International Journal of Computer Vision
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Riemannian Framework for Tensor Computing

International Journal of Computer Vision
Riemannian geometry for the statistical analysis of diffusion tensor data

Signal Processing
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Robust Face Recognition via Sparse Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Region covariance: a fast descriptor for detection and classification

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Compressed sensing

IEEE Transactions on Information Theory
Gabor-Based Region Covariance Matrices for Face Recognition

IEEE Transactions on Circuits and Systems for Video Technology
Face recognition using LDA-based algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recognizing faces from face detector outputs is a hard problem. While existing face recognition (FR) techniques essentially work on pre-processed (cropped and aligned) data, we employ Gabor-based covariance descriptors for recognition from free-form faces (raw face detector outputs). Our recognition algorithm employs a Principal Geodesic Analysis (PGA) of Covariance Descriptors, followed by a transformation on to tangent space where faces are sparsely represented. Employing the kernel trick on this sparse feature space enables upto 10% improvement in recognition accuracy.