Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Writer Identification Using Text Line Based Features
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Writer Identification Using Edge-Based Directional Features
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Using HMM Based Recognizers for Writer Identification and Verification
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
The Relation between the ROC Curve and the CMC
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
A writer identification and verification system
Pattern Recognition Letters
A writer identification and verification system using HMM based recognizers
Pattern Analysis & Applications
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Writer Identification Using Steered Hermite Features and SVM
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Blur Insensitive Texture Classification Using Local Phase Quantization
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
A Feature Selection and Extraction Method for Uyghur Handwriting-Based Writer identification
CINC '09 Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 12.05 |
In this work, we discuss the use of texture descriptors to perform writer verification and identification. We use a classification scheme based on dissimilarity representation, which has been successfully applied to verification problems. Besides assessing two texture descriptors (local binary patterns and local phase quantization), we also address important issues related to the dissimilarity representation, such as the impact of the number of references used for verification and identification, how the framework performs on the problem of writer identification, and how the dissimilarity-based approach compares to other feature-based strategies. In order to meet these objectives, we carry out experiments on two different datasets, the Brazilian forensic letters database and the IAM database. Through a series of comprehensive experiments, we show that both LBP- and LPQ-based classifiers are able to surpass previous results reported in the literature for the verification problem by about 5 percentage points. For the identification problem, the proposed approach using LPQ features is able to achieve accuracies of 96.7% and 99.2% on the BFL and IAM and databases respectively.