Fusing Asynchronous Feature Streams for On-line Writer Identification

In this paper, we present a new approach to improving the performance of a writer identification system by fusing asynchronous feature streams. Different feature streams are extracted from on-line handwritten text acquired from a whiteboard. The feature streams are used to train a text and language independent writer identification system based on Gaussian mixture models (GMMs). From a stroke consisting of n points, n point-based feature vectors and one stroke-based feature vector are extracted. The resulting feature streams thus have an unequal number of feature vectors. We evaluate different methods to directly fuse the feature streams and show that, by means of feature fusion, we can improve the performance of the writer identification system on a data set produced by 200 different writers.