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This paper is about authenticating genuine van Gogh paintings from forgeries.The authentication process depends on two key steps: feature extraction andoutlier detection. In this paper, a geometric tight frame and some simplestatistics of the tight frame coefficients are used to extract features fromthe paintings. Then a forward stage-wise rank boosting is used to select asmall set of features for more accurate classification so that van Goghpaintings are highly concentrated towards some center point while forgeries arespread out as outliers. Numerical results show that our method can achieve86.08% classification accuracy under the leave-one-out cross-validationprocedure. Our method also identifies five features that are much morepredominant than other features. Using just these five features forclassification, our method can give 88.61% classification accuracy which is thehighest so far reported in literature. Evaluation of the five features is alsoperformed on two hundred datasets generated by bootstrap sampling withreplacement. The median and the mean are 88.61% and 87.77% respectively. Ourresults show that a small set of statistics of the tight frame coefficientsalong certain orientations can serve as discriminative features for van Goghpaintings. It is more important to look at the tail distributions of suchdirectional coefficients than mean values and standard deviations. It reflectsa highly consistent style in van Gogh's brushstroke movements, where manyforgeries demonstrate a more diverse spread in these features.

Geometric Tight Frame based Stylometry for Art Authentication of van Gogh Paintings