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A hand profile characterisation approach for biometric identification with contactless hand image acquisition is evaluated. The approach models the shapes of fingers with Point Distribution Models (PDMs), which consist of a mean shape and a number of eigenvectors which describe the main modes of variation of the shape class. The weighted PDM eigenvectors that capture the variation between the input finger shapes and the averaged finger shapes are used as feature vectors. Classification is performed using a least squares support vector machine. Experiments using multiple hand databases demonstrated the advantage of using finger PDMs.