DocumentCode
3488734
Title
FREAK for Real Time Forensic Signature Verification
Author
Malik, Muhammad Imran ; Ahmed, Shehab ; Liwicki, Marcus ; Dengel, Andreas
Author_Institution
German Res. Center for Artificial Intell. (DFKI GmbH), Kaiserslautern, Germany
fYear
2013
fDate
25-28 Aug. 2013
Firstpage
971
Lastpage
975
Abstract
This paper presents a novel signature verification system based on local features of signatures. The proposed system uses Fast Retina Key points (FREAK) which represent local features and are inspired by the human visual system, particularly the retina. To locate local points of interest in signatures, two local key point detectors, i.e., Features from Accelerated Segment Test (FAST) and Speeded-up Robust Features (SURF), have been used and their performance comparison in terms of Equal Error Rate (EER) and time is presented. The proposed system has been evaluated on publicly available dataset of forensic signature verification competition, 4NSigComp2010, which contains genuine, forged, and disguised signatures. The proposed system achieved an EER of 30%, which is considerably very low when compared against all the participants of the said competition. In addition to EER, the proposed system requires only 0.6 seconds on average to verify a 3000*1500 scanned signature. This shows that the proposed system has a potential and suitability for forensic signature verification as well as real time applications.
Keywords
feature extraction; handwriting recognition; image representation; 4NSigComp2010; EER; FAST; FREAK; SURF; disguised signatures; equal error rate; fast retina key points; features from accelerated segment test; forensic signature verification competition; forged signatures; genuine signature; human visual system; local key point detectors; real time forensic signature verification system; signature local feature representation; speeded-up robust features; Conferences; Detectors; Feature extraction; Forensics; Forgery; Real-time systems; Retina; Forensic; Signature; Verification;
fLanguage
English
Publisher
ieee
Conference_Titel
Document Analysis and Recognition (ICDAR), 2013 12th International Conference on
Conference_Location
Washington, DC
ISSN
1520-5363
Type
conf
DOI
10.1109/ICDAR.2013.196
Filename
6628761
Link To Document