Název: Online 3D signature verification by using stereo camera & tablet
Autoři: Dave, Jay
Venkatesh, KS
Jain, Garima
Citace zdrojového dokumentu: WSCG 2015: full papers proceedings: 23rd International Conference in Central Europeon Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 239-246.
Datum vydání: 2015
Nakladatel: Václav Skala - UNION Agency
Typ dokumentu: konferenční příspěvek
URI: wscg.zcu.cz/WSCG2015/CSRN-2501.pdf
ISBN: 978-80-86943-65-7 (print)
978-80-86943-61-9 (CD-ROM)
ISSN: 2464–4617 (print)
2464–4625 (CD-ROM)
Klíčová slova: stereo triangulace;vektorový objekt;dynamické programování;stereo kamera;tlaková digitalizace
Klíčová slova v dalším jazyce: stereo triangulation;feature vector;dynamic programming matching;stereo camera;pressure digitizing
Abstrakt v dalším jazyce: The signature of a person is an important biometric attribute which can be used to authenticate human identity. Conventional online approaches to signature verification only use either a single camera to track the pen tip position or a tablet to extract the dynamic features of the signature, hence the signature has only two spatial dimensions. In this paper we combine data inputs from a pressure sensitive device (tablet digitizer) and stereo vision to record signatures in 3D. Stereo vision from a pair of low cost SONY Eyecam cameras is used to track the pen tip position in x, y, & in z when the pen is off the surface as well as the pen angle with respect to the surface at all times. The digitizing tablet on the other hand, tracks x, y as well as pressure magnitude (which we denote as 􀀀z) when the pen contacts the surface. In all, we record the following parameters as functions of time through the duration of the signature: x;y; z;q;f, where all the linear paramaters are bipolar, with the particular case of z representing motion with positive values and pressure level with negative values. The angular values are two dimensional. The distance between the input signature’s features recorded as a 5-variate parameter time sequence and the template signature’s features whichwere collected during the training phase is computed using Dynamic Time Warping (DTW), and is thresholded to take a decision. While better learning techniques and more intensive experimentation will help suggest improvements, even as of the present, we have a fully working prototype of the system.
Práva: © Václav Skala - UNION Agency
Vyskytuje se v kolekcích:WSCG 2015: Full Papers Proceedings

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