Data/hora: 06/06/2024 01:21:13

Provedor de dados: | 52
País: | Brazil
Título: | An Empirical Evaluation of the Local Texture Description Framework-Based Modified Local Directional Number Pattern with Various Classifiers for Face Recognition
Autores: | Rose,R. Reena; Meena,K.; Suruliandi,A.
Data: | 2016-01-01
Ano: | 2016
Palavras-chave: | Face Recognition; Texture Descriptors; Local Texture Description Framework; Local Texture Description Framework-Based Modified Local Directional Number Pattern; Classifiers; Distance Metrics.
Resumo: | ABSTRACT Texture is one of the chief characteristics of an image. In recent years, local texture descriptors have garnered attention among researchers in describing effective texture patterns to demarcate facial images. A feature descriptor titled Local Texture Description Framework-based Modified Local Directional Number pattern (LTDF_MLDN), capable of encoding texture patterns with pixels that lie at dissimilar regions, has been proposed recently to describe effective features for face images. However, the role of the descriptor can differ with different classifiers and distance metrics for diverse issues in face recognition. Hence, in this paper, an extensive evaluation of the LTDF_MLDN is carried out with an Extreme Learning Machine (ELM), a Support Vector Machine (SVM) and a Nearest Neighborhood Classifier (NNC) which uses Euclidian, Manhattan, Minkowski, G-statistics and chi-square dissimilarity metrics to illustrate differences in performance with respect to assorted issues in face recognition using six benchmark databases. Experimental results depict that the proposed descriptor is best suited with NNC for general case and expression variation, whereas, for the other facial variations ELM is found to produce better results.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300405
Editor: | Instituto de Tecnologia do Paraná - Tecpar
Relação: | 10.1590/1678-4324-2016161057
Formato: | text/html
Fonte: | Brazilian Archives of Biology and Technology  v.59 n.spe2 2016
Fonte: | info:eu-repo/semantics/openAccess