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首页> 外文会议>International conference on machine vision >Age and Gender Estimation Using Region-SIFT and Multi-Layered SVM
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Age and Gender Estimation Using Region-SIFT and Multi-Layered SVM

机译：使用Region-SIFT和多层SVM进行年龄和性别估计

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摘要

In this paper, we propose an age and gender estimation framework using the region-SIFT feature and multi-layered SVM classifier. The suggested framework entails three processes. The first step is landmark based face alignment. The second step is the feature extraction step. In this step, we introduce the region-SIFT feature extraction method based on facial landmarks. First, we define sub-regions of the face. We then extract SIFT features from each sub-region. In order to reduce the dimensions of features we employ a Principal Component Analysis (PCA) and a Linear Discriminant Analysis (LDA). Finally, we classify age and gender using a multi-layered Support Vector Machines (SVM) for efficient classification. Rather than performing gender estimation and age estimation independently, the use of the multi-layered SVM can improve the classification rate by constructing a classifier that estimate the age according to gender. Moreover, we collect a dataset of face images, called by DGIST_C, from the internet. A performance evaluation of proposed method was performed with the FERET database, CACD database, and DGIST_C database. The experimental results demonstrate that the proposed approach classifies age and performs gender estimation very efficiently and accurately.
机译：在本文中，我们提出了一个使用区域SIFT特征和多层SVM分类器的年龄和性别估计框架。建议的框架包含三个过程。第一步是基于界标的面部对齐。第二步是特征提取步骤。在这一步中，我们介绍了基于面部标志的区域SIFT特征提取方法。首先，我们定义面部的子区域。然后，我们从每个子区域中提取SIFT特征。为了减小特征的尺寸，我们采用了主成分分析（PCA）和线性判别分析（LDA）。最后，我们使用多层支持向量机（SVM）对年龄和性别进行有效分类。多层SVM的使用不是构建独立的性别估计和年龄估计方法，而是通过构建根据性别估计年龄的分类器来提高分类率。此外，我们从互联网上收集了由DGIST_C调用的面部图像数据集。使用FERET数据库，CACD数据库和DGIST_C数据库对所提出方法的性能进行了评估。实验结果表明，该方法对年龄进行了分类，并且非常有效，准确地进行了性别估计。

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