Facial expression recognition using wavelet transform and local binary pattern

A. Alsubari, D. N. Satange, R. Ramteke
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引用次数: 12

Abstract

This paper aims to experimental evaluation of different methodologies to recognize human face based on different facial expression. The face and facial images were captured locally, as the experiment is aimed to be done in India domain. The features were extracted based on two techniques, viz, Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The range of extracted feature is 150,300,600,1200 and 2400. Further, mean and standard deviation are computed for feature vector generation. Support Vector Machine (SVM) is used for classification/recognition. The experiment was carried out on different range of features as 160×15 samples. The result varies from 72% to 100% for various ranges of features. The performance of the proposed system is found to be satisfactory as compared to the existing system.
基于小波变换和局部二值模式的面部表情识别
本文旨在对基于不同面部表情的人脸识别方法进行实验评价。人脸和面部图像是在当地捕获的,因为实验的目标是在印度地区进行。基于离散小波变换(DWT)和局部二值模式(LBP)两种技术提取特征。提取的特征范围为150,300,600,1200和2400。进一步,计算平均值和标准差以生成特征向量。支持向量机(SVM)用于分类/识别。实验以不同范围的特征为160×15样本进行。对于不同的特征范围,结果从72%到100%不等。与现有系统相比,建议系统的性能令人满意。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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