IEEE Trans Image Process - Ordinal feature selection for iris and palmprint recognition.

Tópicos

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Resumo

Ordinal measures have been demonstrated as an effective feature representation model for iris and palmprint recognition. However, ordinal measures are a general concept of image analysis and numerous variants with different parameter settings, such as location, scale, orientation, and so on, can be derived to construct a huge feature space. This paper proposes a novel optimization formulation for ordinal feature selection with successful applications to both iris and palmprint recognition. The objective function of the proposed feature selection method has two parts, i.e., misclassification error of intra and interclass matching samples and weighted sparsity of ordinal feature descriptors. Therefore, the feature selection aims to achieve an accurate and sparse representation of ordinal measures. And, the optimization subjects to a number of linear inequality constraints, which require that all intra and interclass matching pairs are well separated with a large margin. Ordinal feature selection is formulated as a linear programming (LP) problem so that a solution can be efficiently obtained even on a large-scale feature pool and training database. Extensive experimental results demonstrate that the proposed LP formulation is advantageous over existing feature selection methods, such as mRMR, ReliefF, Boosting, and Lasso for biometric recognition, reporting state-of-the-art accuracy on CASIA and PolyU databases.

Resumo Limpo

ordin measur demonstr effect featur represent model iri palmprint recognit howev ordin measur general concept imag analysi numer variant differ paramet set locat scale orient can deriv construct huge featur space paper propos novel optim formul ordin featur select success applic iri palmprint recognit object function propos featur select method two part ie misclassif error intra interclass match sampl weight sparsiti ordin featur descriptor therefor featur select aim achiev accur spars represent ordin measur optim subject number linear inequ constraint requir intra interclass match pair well separ larg margin ordin featur select formul linear program lp problem solut can effici obtain even largescal featur pool train databas extens experiment result demonstr propos lp formul advantag exist featur select method mrmr relieff boost lasso biometr recognit report stateoftheart accuraci casia polyu databas

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