Theory of keyblock-based image retrieval
ACM Transactions on Information Systems (TOIS)
A Smoothness Index Based Approach for Off-Line Signature Verification
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
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
Offline Geometric Parameters for Automatic Signature Verification Using Fixed-Point Arithmetic
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten Signature Verification Using Image Invariants and Dynamic Features
CGIV '06 Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation
Off-Line Signature Recognition and Verification by Kernel Principal Component Self-Regression
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
An Efficient Fast Algorithm to Generate Codebook for Vector Quantization
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
Fast codebook search algorithm for vector quantization using sorting technique
Proceedings of the International Conference on Advances in Computing, Communication and Control
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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Handwritten Signatures are one of the widely used biometric traits for document authentication as well as human authorization. Various techniques have been implemented for Automatic Signature Recognition. In this paper we discuss the application of vector quantization to the problem of signature recognition. Vector quantization based methodology is used here to detect intra and inter-class variations in signatures. Here we discuss a method for the codebook generation; this method is fast and simple. We use the codebook to generate a codevector histogram specific to the signature template. The spatial moments related to the codevectors are also calculated. These parameters are used to classify the signature. Feasibility of this technique for signature recognition is discussed in his paper.