Comput Math Methods Med - Membership-degree preserving discriminant analysis with applications to face recognition.

Tópicos

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Resumo

In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzy k-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.

Resumo Limpo

pattern recognit featur extract techniqu wide employ reduc dimension highdimension data paper propos novel featur extract algorithm call membershipdegre preserv discrimin analysi mpda base fisher criterion fuzzi set theori face recognit propos algorithm membership degre sampl particular class first calcul fuzzi knearest neighbor fknn algorithm character similar sampl class center membership degre incorpor definit betweenclass scatter withinclass scatter featur extract criterion via maxim ratio betweenclass scatter withinclass scatter appli experiment result orl yale feret face databas demonstr effect propos algorithm

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