Recognition of facial expressions using associative memory

Yuri Iwanot, Masahide YoneyamaS, Katsuhiko Shirait
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引用次数: 7

Abstract

We extract the movements of 8/spl times/10 regions of the face by using optical flow and simplify the information by considering whether a particular region of the face moved or not. By applying this information in a Hopfield neural network based on a discrete model, we try to absorb the differences between individuals and the degree of feelings effectively. We carried out an experiment with 144 examples of image data. The results of before and after applying the data in the Hopfield neural network were 61.8% and 71.5% respectively.
利用联想记忆识别面部表情
利用光流提取人脸8/spl次/10个区域的运动,并考虑人脸某一特定区域是否运动,对信息进行简化。通过将这些信息应用到基于离散模型的Hopfield神经网络中,我们试图有效地吸收个体之间的差异和感受程度。我们用144个图像数据样本进行了实验。在Hopfield神经网络中应用该数据前后的结果分别为61.8%和71.5%。
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