Fingerprint Matching using Small Sensors

Abstract: Fingerprint software is widely used in applications such as smartphones. There has to be some overlap between two fingerprint samples in order for them to be considered as belonging to the same finger. Determining if this overlap exists is normally not a problem if a fingerprint sensor is big enough to capture a sample from the whole finger at once. However, if the sensor is small it might capture samples from different parts of the same finger such that there is no overlap. To find a predictor one needs a training set but constructing the set using small sensors lead to a highly noisy set. This project examines some methods to filter the noise. None of the filtering methods provided a conclusive improvement on all datasets compared to the already implemented method. The most promising methods, however, includes a substitution of the SVM algorithm with S3VM and either to use no filtering, random downsampling of the majority class or a recursive filter.