Handwritten Chinese signature verification based on attributed string matching of stroke linkage order

We propose an attributed string matching approach based on the writing sequences of an input signature for Chinese signature verification. It is impossible to find features of a signature that are invariant with respect to individual writing style. The aim of our research is to find a particular feature set that will exhibit small intraclass variance. A signer tends to connect consecutive strokes in a constant sequences when signing a signature, especially for Chinese signatures. Therefore, the writing sequences (stroke order) of a signature can be regarded as the personal signature feature. In order to obtain an attributed string that will be used in the string matching similarity calculation, we must split an input signature into several segments from the corners of strokes at first. Since the wavelet transform has been used in the field of edge detection and corner detection for a long time, we can use it as a tool for corner detection to find an optimal segmentation set. Therefore, an attributed string for consecutive segments can be calculated after the signature has been split into several segments suitably by means of a wavelet transform. The elements in the attributed vector include relative angle of adjoining segments, the direction code of the segment, writing duration time of the segment, and the length of the segment. The total relative angle can also be summarized over all of the relative angles of segments to result in another individual feature. This total relative angle can be used to filter the rougher forgery signatures. Our attributed string matching method is also based on the extraction of irreducible characteristic points. The experimental results show a very excellent discrimination capability