title: Online handwritten signature verification using neural network classifier based on principal component analysis.
abstract: One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%.
authors.affiliation: Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
authors.forename: Vahab
authors.initials: V
authors.lastname: Iranmanesh
authors.affiliation: Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
authors.forename: Sharifah Mumtazah Syed
authors.initials: SM
authors.lastname: Ahmad
authors.affiliation: Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
authors.forename: Wan Azizun Wan
authors.initials: WA
authors.lastname: Adnan
authors.affiliation: Department of Systems and Networking, Universiti Tenaga Nasional, Jalan IKRAM-Uniten, 43000 Kajang, Malaysia.
authors.forename: Salman
authors.identifier: 0000-0002-2040-4454
authors.initials: S
authors.lastname: Yussof
authors.affiliation: Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
authors.forename: Olasimbo Ayodeji
authors.initials: OA
authors.lastname: Arigbabu
authors.affiliation: Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
authors.forename: Fahad Layth
authors.initials: FL
authors.lastname: Malallah
mesh_terms: D000465:Algorithms; D016494:Computer Security; D006236:Handwriting; D063867:Identity Theft / Q000517:prevention & control; D020407:Internet; D016571:Neural Networks, Computer