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 ℓ_0 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 ℓ_1 norm-based sparse representation classifier (SRC).
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