A similarity-based neural network for facial expression analysis
A similarity-based neural network for facial expression analysis
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DOI：
10.1016/j.patrec.2007.01.005
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发表时间：
2007-07
期刊：
影响因子：
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通讯作者：
Kenji Suzuki;H. Yamada;S. Hashimoto
中科院分区：
文献类型：
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作者：
Kenji Suzuki;H. Yamada;S. Hashimoto
在本文中，我们介绍了一种新的模型，用于测量人类的主观评价，使用相关学习的基础上，基于相似性的多层感知器。这项工作的目的是实现一个多维的感知缩放相关联的物理特征的脸，其语义向量在一个低维空间。与传统的多层感知器不同，它从一组输入特征向量和期望的输出中学习，所提出的网络可以获得输入特征向量和一对对象的输出之间的非线性映射及其期望的相关性（距离）。我们进行了面部表情分析与面部表情的线条画图像的心理模型和一个真实的图像集。关于语义空间的构建，所提出的方法不仅表现出良好的性能相比，传统的统计方法，但也能够项目在训练阶段没有使用的新数据。我们将展示一些实验结果，并讨论所获得的映射函数。
In this paper, we introduce a novel model for the measuring of human subjective evaluation by using Relevance Learning based on a similarity-based multilayer perceptron. This work aims to achieve a multidimensional perceptual scaling that associates the physical features of a face with its semantic vector in a low-dimensional space. Unlike the conventional multilayer perceptron that learns from a set of an input feature vector and the desired output, the proposed network can obtain a nonlinear mapping between the input feature vectors and the outputs from a pair of objects and their desired relevance (distance). We conducted a facial expression analysis with both a psychological model of line-drawing image of facial expression and a real image set. Regarding the construction of semantic space, the proposed approach not only shows a good performance as compared with the conventional statistical method but is also able to project new data that are not used during the training phase. We will show some experimental results and discuss the obtained mapping function.