Banca de DEFESA: JULLIANA CAROLINE GONÇALVES DE ARAÚJO SILVA MARQUES

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : JULLIANA CAROLINE GONÇALVES DE ARAÚJO SILVA MARQUES
DATE: 29/01/2021
TIME: 10:00
LOCAL: Google Meets
TITLE:

The impact of feature selection methods on online handwritten signature by using clustering-based analysis

KEY WORDS:

online handwritten signature, feature selection, clustering, SVC2004, xLongSignDB.

PAGES: 66
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Metodologia e Técnicas da Computação
SPECIALTY: Processamento Gráfico (Graphics)
SUMMARY:

Handwritten signature is one of the oldest and most accepted biometric authentication methods for human identity establishment in society. With the popularisation of computers and, consequently, computational biometric authentication systems, the signature was chosen for being one of the biometric traits of an individual that is likely to be relatively unique for every person. However, when dealing with biometric data, including signature data, problems related to high dimensional space, can be generated.  Among other issues, irrelevant, redundant data and noise are the most significant, as they result in a decreased of identification accuracy. Thus, it is necessary to reduce the space by selecting the smallest set of features that contain the most discriminative features, increasing the accuracy of the system. In this way, our proposal in this work is to analyse the impact of feature selection on individuals identification accuracy based on the handwritten online signature. For this, we will use two well-known online signature databases: SVC2004 and xLongSignDB. For the feature selection process, we have applied two filter and one wrapper methods. Then, the resulted datasets are evaluated by classification algorithms and validated with a clustering technique. Besides, we have used a statistical test to corroborate our conclusions. Experiments presented satisfactory results when using a smaller number of features which are more representative, showing that we reached an average accuracy of over 98\% for both datasets which were validated with the clustering methods, which achieved an average accuracy over 80\% (SVC2004) and 70\% (xLongSignDB).

BANKING MEMBERS:
Presidente - 2524467 - MARJORY CRISTIANY DA COSTA ABREU
Interna - 2859606 - SILVIA MARIA DINIZ MONTEIRO MAIA
Externo à Instituição - PLACIDO ANTONIO DE SOUZA NETO - IFRN