Yesterday we were discussing how to enable user logins with something that they draw such as their signature on a signature pad. Efficient image processing algorithms can then compare signatures. Moreover, what people draw on the signature pads is completely their call and can even handwrite passwords instead of signature. Since the data is private both at rest and transit, this cannot be divulged with anybody else and provides a layer of security on top of the known passwords.  Signature detection and segmentation is a known field of study and techniques involve shape matching. While some of this processing involve offline techniques, there are online techniques also mentioned in the associated literature.  Moreover, MYCT-Signature corpus, Susig database and GPDS-960 provide well known databases for evaluating algorithms.  For example, one method of non-rigid shape matching involves a spatial histogram aka shape context computed for each point which describes the distributions of the relative positions of all remaining points. The correspondences between points are solved through weighted bipartite graph matching before the signatures are matched. Another method of non-rigid shape matching formulates it as an optimization problem that preserves a local neighborhood structure. This method has an intuitive graph matching interpretation where each point represents a vertex and two vertices are considered connected in the graph if they are neighbors. The problem of finding optimal match between shapes is therefore equivalent to maximizing the number of matched edges between their corresponding graphs under a one-to-one matching constraint. In this optimization approach, an iterative framework is used  to estimate the correspondences and the transformation. In each iteration, graph matching is initialized using shape context distance and subsequently updated through relaxation labeling which is a well-known formal method of expressing low level contextual information, and applying it to complete the extraction of image features. 
Image processing generally involves multiple subsequent stages of processing the images. Signatures have the nice property that they are like the results of sobel edge detection and the edges are expected to be more continuous in their formation.  Moreover, signature pads are  small images, with similar curves and accents and purely black and white, so they are near consistent and this helps with their processing.