An Off-Line Signature Verification System using Hidden Markov Model and Cross-Validation
SIBGRAPI '00 Proceedings of the 13th Brazilian Symposium on Computer Graphics and Image Processing
An Image Similarity Measure Based on Graph Matching
SPIRE '00 Proceedings of the Seventh International Symposium on String Processing Information Retrieval (SPIRE'00)
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Investigation of Off-Line Japanese Signature Verification Using a Pattern Matching
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Online signature verification using a new extreme points warping technique
Pattern Recognition Letters
Gaussian Mixture Models for on-line signature verification
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Detecting image near-duplicate by stochastic attributed relational graph matching with learning
Proceedings of the 12th annual ACM international conference on Multimedia
Identity authentication using improved online signature verification method
Pattern Recognition Letters
Illumination Invariant Elastic Bunch Graph Matching for Efficient Face Recognition
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
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The biometric system is used to identify a person depending on his physiological or behavioral characteristics. Signature verification is a commonly accepted biometric method and is widely used for banking transactions. In this paper, we propose Offline Signature Authentication using Cross-validated Graph Matching (OSACGM) algorithm. The signatures are pre-processed in which signature extraction method is used to obtain high resolution for smaller normalization box. The similarity measure between two signatures in the database is determined by (i) constructing a bipartite graph G, (ii) obtaining complete matching in G and (iii) finding minimum Euclidean distance by Hungarian method. An optimum decision threshold value is determined using Cross-validation technique to select reference signatures. The test feature is extracted from the given test signature by pre-processing. Then the test feature is compared with the threshold value to authenticate the test signature. Compared to the existing algorithm, our algorithm gives better Equal Error Rate (EER) for skilled and random forgeries.