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
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Misalignment-robust face recognition
IEEE Transactions on Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fixed-point iterative schema for error minimization in k-sparse decomposition
ISSPIT '11 Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology
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In this paper, we present a new approach for face recognition that is robust against both poorly defined and poorly aligned training and testing data even with few training samples. Working in the conventional feature space yielded by the Fisher's Linear Discriminant analysis, it uses a recent algorithm for sparse representation, namely k-LiMapS, as general classification criterion. Such a technique performs a local ℓ0 pseudo-norm minimization by iterating suitable parametric nonlinear mappings. Thanks to its particular search strategy, it is very fast and able to discriminate among separated classes lying in the low-dimension Fisherspace. Experiments are carried out on the FRGC version 2.0 database showing good classification capability even when compared with the state-of-the-art ℓ1 norm-based sparse representation classifier (SRC).