title: Online signature verification with support vector machines based on LCSS kernel functions.
abstract: In this paper, a new technique for online signature verification or identification is proposed. The technique integrates a longest common subsequences (LCSS) detection algorithm which measures the similarity of signature time series into a kernel function for support vector machines (SVM). LCSS offers the possibility to consider the local variability of signals such as the time series of pen-tip coordinates on a graphic tablet, forces on a pen, or inclination angles of a pen measured during a signing process. Consequently, the similarity of two signature time series can be determined in a more reliable way than with other measures. A proprietary database with signatures of 153 test persons and the SVC 2004 benchmark database are used to show the properties of the new SVM-LCSS. We investigate its parameterization and compare it to SVM with other kernel functions such as dynamic time warping (DTW). Our experiments show that SVM with the LCSS kernel authenticate persons very reliably and with a performance which is significantly better than that of the best comparing technique, SVM with DTW kernel.
authors.affiliation: Elektrobit Corporation, Munich, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Gruber
authors.forename: Thiemo
authors.initials: T
authors.lastname: Gruber
authors.forename: Sebastian
authors.initials: S
authors.lastname: Krinninger
authors.forename: Bernhard
authors.initials: B
authors.lastname: Sick
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D001330:Electronic Data Processing / Q000379:methods*; D006236:Handwriting; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011932:Reading