Writer Identification Using Handwritten Cursive Texts and Single Character Words
| Autor: | Tobias Kutzner, Ingrid Bonninger, Carlos M. Travieso, Wolf-Dietrich Plath, Matthias Gebhard, Carlos F. Pazmiño-Zapatier |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Biometrics
Computer Networks and Communications Computer science password identification lcsh:TK7800-8360 02 engineering and technology computer.software_genre Naive Bayes classifier reduction feature Discriminative model Handwriting 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Electrical and Electronic Engineering Cursive on-line handwriting features online strokes business.industry lcsh:Electronics 020206 networking & telecommunications Identification (information) ComputingMethodologies_PATTERNRECOGNITION Hardware and Architecture Control and Systems Engineering writer verification Signal Processing 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing handwriting |
| Zdroj: | Electronics Volume 8 Issue 4 Electronics, Vol 8, Iss 4, p 391 (2019) |
| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics8040391 |
| Popis: | One of the biometric methods in authentication systems is the writer verification/identification using password handwriting. The main objective of this paper is to present a robust writer verification system by using cursive texts as well as block letter words. To evaluate the system, two datasets have been used. One of them is called Secure Password DB 150, which is composed of 150 users with 18 samples of single character words per user. Another dataset is public and called IAM online handwriting database, and it is composed of 220 users of cursive text samples. Each sample has been defined by a set of features, composed of 67 geometrical, statistical, and temporal features. In order to get more discriminative information, two feature reduction methods have been applied, Fisher Score and Info Gain Attribute Evaluation. Finally, the classification system has been implemented by hold-out cross validation and k-folds cross validation strategies for three different classifiers, K-NN, Naï ve Bayes and Bayes Net classifiers. Besides, it has been applied for verification and identification approaches. The best results of 95.38% correct classification are achieved by using the k-nearest neighbor classifier for single character DB. A feature reduction by Info Gain Attribute Evaluation improves the results for Naï ve Bayes Classifier to 98.34% for IAM online handwriting DB. It is concluded that the set of features and its reduction are a strong selection for the based-password handwritten writer identification in comparison with the state-of-the-art. |
| Databáze: | OpenAIRE |
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