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	Provedor de dados BABT (2) Autor Meena,K. (2) Malarvizhi,N. (1) Rose,R. Reena (1) Suruliandi,A. (1) Palavra-chave Classification Support Vector Machine NN classifier (1) Classifiers (1) Distance Metrics (1) Face Recognition (1) Local Texture Description Framework (1) Local Texture Description Framework-Based Modified Local Directional Number Pattern (1) Multimodal Biometrics Face Finger print Iris Contourlet transform (1) Texture Descriptors (1) Mais... Tipo do documento Info:eu-repo/semantics/article (2) Ano 2016 (2) País Brazil (2) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última An Empirical Evaluation of the Local Texture Description Framework-Based Modified Local Directional Number Pattern with Various Classifiers for Face Recognition BABT Rose,R. Reena; Meena,K.; Suruliandi,A.. 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... Tipo: Info:eu-repo/semantics/article Palavras-chave: Face Recognition; Texture Descriptors; Local Texture Description Framework; Local Texture Description Framework-Based Modified Local Directional Number Pattern; Classifiers; Distance Metrics. Ano: 2016 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300405 An Efficient Human Identification through MultiModal Biometric System BABT Meena,K.; Malarvizhi,N.. ABSTRACT Human identification is essential for proper functioning of society. Human identification through multimodal biometrics is becoming an emerging trend, and one of the reasons is to improve recognition accuracy. Unimodal biometric systems are affected by various problemssuch as noisy sensor data,non-universality, lack of individuality, lack of invariant representation and susceptibility to circumvention.A unimodal system has limited accuracy. Hence, Multimodal biometric systems by combining more than one biometric feature in different levels are proposed in order to enhance the performance of the system. A supervisor module combines the different opinions or decisions delivered by each subsystem and then make a final decision. In this paper, a... Tipo: Info:eu-repo/semantics/article Palavras-chave: Multimodal Biometrics Face Finger print Iris Contourlet transform; Classification Support Vector Machine NN classifier. Ano: 2016 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300403 Registros recuperados: 2 Primeira ... 1 ... Última

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