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首页> 外文会议>International conference on machine vision >Gender Classification from Face Images by Using Local Binary Pattern and Gray-Level Co-Occurrence Matrix
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Gender Classification from Face Images by Using Local Binary Pattern and Gray-Level Co-Occurrence Matrix

机译：基于局部二值模式和灰度共生矩阵的人脸图像性别分类

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摘要

Gender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed automatically from facial images. In order to classify gender, we propose a combination of features that have been extracted face, eye and lip regions by using a hybrid method of Local Binary Pattern and Gray-Level Co-Occurrence Matrix. The features have been extracted from automatically obtained face, eye and lip regions. All of the extracted features have been combined and given as input parameters to classification methods (Support Vector Machine, Artificial Neural Networks. Naive Bayes and k-Nearest Neighbor methods) for gender classification. The Nottingham Scan face database that consists of the frontal face images of 100 people (50 male and 50 female) is used for this purpose. As the result of the experimental studies, the highest success rate has been achieved as 98% by using Support Vector Machine. The experimental results illustrate the efficacy of our proposed method.
机译：性别是人机交互过程和识别的重要步骤。人脸图像是确定性别的重要来源之一。在本研究中，性别分类是根据面部图像自动执行的。为了对性别进行分类，我们提出了使用局部二进制模式和灰度共生矩阵的混合方法提取的面部，眼睛和嘴唇区域的特征的组合。从自动获得的脸部，眼睛和嘴唇区域中提取了特征。所有提取的特征均已合并，并作为分类方法（支持向量机，人工神经网络，朴素贝叶斯方法和k-最近邻方法）的输入参数进行性别分类。为此，使用了由100个人（50位男性和50位女性）的正面图像组成的Nottingham Scan面部数据库。实验研究的结果，使用支持向量机获得了最高的成功率，达到98％。实验结果说明了我们提出的方法的有效性。

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