Facial Expression Recognition Using Neural Network Trained with Zernike Moments
Facial Expression Recognition Using Neural Network Trained with Zernike Moments
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使用经过 Zernike 矩训练的神经网络进行面部表情识别
DOI：
10.1109/icaiet.2014.39
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Ramdani
中科院分区：
文献类型：
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作者：
Mohammed Saaidia;Narima Zermi;M. Ramdani
本文采用神经网络分类方法对人脸表情进行识别。被处理的表情是六个最相关的面部表情和一个中性的。该操作分三个步骤实施。首先，将使用Zernike矩训练的神经网络应用于著名的Yale和JAFFE数据库图像集来执行人脸检测。在第二步中，检测到的脸进行处理，通过计算Zernike矩的向量进行表征阶段。最后，训练一个反向传播神经网络来区分呈现的人脸的七种情绪状态。最后，在著名的JAFEE和YALE数据库上对方法的性能进行了评估。
Neural network classifying method is used in this work to perform facial expression recognition. The processed expressions were the six most pertinent facial expressions and the neutral one. This operation was implemented in three steps. First, a neural network, trained using Zernike moments, was applied to the set of the well known Yale and JAFFE database images to perform face detection. In the second step, detected faces are processed to perform the characterization phase through computed vectors of Zernike moments. At last step, a back propagation neural network was trained to distinguish between the seven emotion's states of a presented face. Finally, method performances were evaluated on the well known JAFEE and YALE database.
基于特征脸的人脸建模与识别
DOI：
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发表时间：
2003
期刊：
IPSJ SIG Technical Reports Vol. CVIM-139
影响因子：
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作者：
T.;Shakunaga;F.;Sakaue;Y.;Matsubara
通讯作者：
Matsubara