Off-Line Signature Verification by Local Granulometric Size Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keystroke dynamics as a biometric for authentication
Future Generation Computer Systems - Special issue on security on the Web
Wavelet-based off-line handwritten signature verification
Computer Vision and Image Understanding
Pattern Recognition Letters
Off-line Signature Verification Using HMM for Random, Simple and Skilled Forgeries
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Online signature verification using a new extreme points warping technique
Pattern Recognition Letters
Handwriting Authentication by Envelopes of Sound Signatures
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Offline Geometric Parameters for Automatic Signature Verification Using Fixed-Point Arithmetic
IEEE Transactions on Pattern Analysis and Machine Intelligence
Glove-Based Approach to Online Signature Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-line signature verification using LPC cepstrum and neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper introduces a real-time system for verifying handwritten signatures that relies on a hybrid methodology, for which consistency checking is performed prior to enrolling signatures for further processing. Only the best six signatures are retained out of 10 signatures for each signer, during the enrollment phase, based on the deviation in both the total signing time and the binary pattern of the pen movement. The proposed system consists of three consecutive phases, where the first one is an online approach that is quite similar to the enrollment phase, and acts as an initial bottleneck for the verification process so that simple forgeries are quickly filtered out. The second phase uses a combination of neural networks and linear predictive coding to construct a majority voting committee in a pattern recognition context to decide on the authenticity of the signatures that passed the first phase of the verification process. The third phase is an offline technique that processes the real-time data features after converting them into stationary image frames. A digitizing tablet was used to collect eight features during the implementation of the proposed system resulting in a 2.9% for the false acceptance rate and 8.8% for the false rejection rate.