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One-Class SVM applied to identification of Diffractive Optical Variable Image

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3 Author(s)
Jing Shao ; School of Beijing Normal University, China ; Xinyu Chen ; Ping Guo

In this paper, we propose a method by engaging the one class support vector machine (OC-SVM) in the identification of diffractive optically variable images (DOVIs). OC-SVM, as a special SVM, can solve the problems of high-dimensional data sets and small sample size (SSS) with positive and negative unbalance training data. Image feature matrix is built by extracting image features from texture aspects. OC-SVM can be trained with the high-dimensional matrix directly, and does not have to reduce the dimensionality of feature matrix as the usual methods. The experiment results show the effectiveness of the proposed approach against linear discriminant analysis. Considering time cost and correct classification rate, OC-SVM is suitable for the identification of DOVIs.

Published in:

2009 3rd International Conference on Anti-counterfeiting, Security, and Identification in Communication

Date of Conference:

20-22 Aug. 2009