Optimization of LBP parameters

Lóderer, Marek; Pavlovičová, Jarmila

doi:10.1109/elmar.2014.6923329

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…As in the case of biological evolution, it undergoes genetic operations such as crossover, mutation, and selection. A vast amount of literature on using GA for solving the FR problem exists [Yankun and Chongqing 2002;Harand et al 2004;Karungaru et al 2004;Fan and Verma 2004;Zheng et al 2005;Liu and Wang 2006;Kim et al 2006;Tan et al 2007;Li et al 2010;Zhao 2010;Venkatesan and Rao Madane 2010;Valdez et al 2011;Gamarra and Quintero 2013;Nam and Miura 2013;Shih and Liu 2005;Loderer and Pavlovicova 2014]. Generally, each chromosome encodes a feature subset of the face image.…”

Section: Genetic Algorithms (Gas)mentioning

confidence: 98%

The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

et al. 2015

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An increased number of bio-inspired face recognition systems have emerged in recent decades owing to their intelligent problem-solving ability, flexibility, scalability, and adaptive nature. Hence, this survey aims to present a detailed overview of bio-inspired approaches pertaining to the advancement of face recognition. Based on a well-classified taxonomy, relevant bio-inspired techniques and their merits and demerits in countering potential problems vital to face recognition are analyzed. A synthesis of various approaches in terms of key governing principles and their associated performance analysis are systematically portrayed. Finally, some intuitive future directions are suggested on how bio-inspired approaches can contribute to the advancement of face biometrics in the years to come.

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Section: Genetic Algorithms (Gas)mentioning

confidence: 98%

The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

et al. 2015

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“…An optimal selection of discriminative features is an essential condition of efficient face recognition and at the same time it enables memory and time complexity reduction. In our previous analysis [23] we arrived at a conclusion that the recognition accuracy depends not only on the selected LBP feature type, but also on the size and proportions of blocks used in the LBP-feature space for a histogram construction. Because of that, we used the size and proportions of blocks in the LBP-feature space as an optimization parameter.…”

Section: Face Databasementioning

confidence: 99%

Comparative Study of Local Binary Pattern Derivatives for Low Size Feature Vector Representation in Face Recognition

Lóderer¹,

Pavlovičová²,

Oravec³

2018

ACTA POLYTECH HUNG

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In this paper, Local Binary Patterns (LBP) and their derivatives, like Local Ternary Patterns (LTP), Local Gradient Patterns (LGP), Non-Redundant Local BinaryPatterns (NRLBP) and multi-scale images processed by LBPs, are evaluated in order to find the optimal features for the automatic face recognition system. The comparison of LBP and its variations is performed based on the recognition accuracy. The genetic algorithm optimizes a criterion function, which combines four parameters, such as LBP feature type, feature image processing type, and feature dimension and distance measure. The evaluation was performed on four different face databases. The proposed methodology can be applied in various kinds of recognition, such as facial expression recognition. The main strength of this paper is the design methodology for the selection of the most discriminative features, in accordance with the desired feature vector length and face recognition accuracy.

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“…Also, the author proposed an aggregative tness function, which combines classi cation accuracy and the number of features in a single equation. Loderer and Pavlovičová (2014) proposed to optimize the parameters of Local Binary Patterns (LBP) such as type of pattern, size of blocks, distance measure, and the dimension of histograms using GA. The chromosome is represented as a sequence of values which will be optimized.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…After ltering the results through objective, inclusion and exclusion criteria, seventy three relevant works are gathered for SLR purpose. Some of the bio-inspired algorithms used in 2D FR systems for di erent purposes are template matching (Chidambaram et al;, classi cation (Nebti and Boukerram;, parameters optimization Shen et al;Fernández-Martínez and Cernea;Loderer and Pavlovičová;, and feature selection (Khadhraoui et al;Farag et al;.…”

Section: Introductionmentioning

confidence: 99%

Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

Plichoski

Chidambaram

Parpinelli

2018

RBCA

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A wide range of approaches for 2D face recognition (FR) systems can be found in the literature due to its high applicability and issues that need more investigation yet which include occlusion, variations in scale, facial expression, and illumination. Over the last years, a growing number of improved 2D FR systems using Swarm Intelligence and Evolutionary Computing algorithms have emerged. The present work brings an up-to-date Systematic Literature Review (SLR) concerning the use of Swarm Intelligence and Evolutionary Computation applied in 2D FR systems. Also, this review analyses and points out the key techniques and algorithms used and suggests some directions for future research.Key words: Bio-inspired algorithms; Evolutionary algorithms; Face Recognition; Natural Computing; Optimization; Swarm algorithms ResumoUma ampla gama de abordagens para sistemas de reconhecimento facial (FR) 2D pode ser encontrada na literatura devido a sua alta aplicabilidade e também por questões que necessitam de mais investigação, incluindo oclusão, variações de escala, expressão facial e iluminação. Nos últimos anos, um número crescente de sistemas de FR 2D usando algoritmos de Inteligência de Enxame e Computação Evolucionária surgiram. O presente trabalho traz uma Revisão Sistemática de Literatura (SLR) atualizada sobre o uso da Inteligência de Enxame e Computação Evolucionária aplicada em sistemas de FR 2D. Além disso, esta revisão analisa e aponta as principais técnicas e algoritmos utilizados e sugere alguns direcionamentos para pesquisas futuras.

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Optimization of LBP parameters

Cited by 8 publications

References 8 publications

The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

Comparative Study of Local Binary Pattern Derivatives for Low Size Feature Vector Representation in Face Recognition

Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

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