Method of Off-line Signature Recognition Based on Improved LPP and ECOC-SVMS

doi:10.3969/j.issn.1006-2475.2018.10.015

Abstract

Abstract: A method of off-line signature recognition based on locality preserving projection(LPP) and Error Correcting Output Code support vector machine(ECOC-SVMS) is proposed. After selecting multiple features from preprocessed signature images, high dimensionality feature vectors are constructed. Then, an improved LPP method is used to extract effect features and reduce dimensionality. A multi-classification classifier based on Hadamard code ECOC-SVMS is used to deal with signature recognition problem. A proximate probability output of SVMS is employed to improve the decoding processing of ECOC framework to enhance the performance of multi-classification. The experiment result shows that the proposed method is feasible and effective.

Key words: off-line signature recognition, locality preserving projection, error correcting output code support vector machine

CLC Number:

TP317.4

JIANG Qing-yun. Method of Off-line Signature Recognition Based on Improved LPP and ECOC-SVMS[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.10.015.

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