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首页> 外文会议>Mathematical models for engineering science >Virtual Sample Generation Using Concurrent-Self-Organizing Maps and Its Application for Facial Expression Recognition
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Virtual Sample Generation Using Concurrent-Self-Organizing Maps and Its Application for Facial Expression Recognition

机译：使用并发自组织映射的虚拟样本生成及其在面部表情识别中的应用

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摘要

This paper is dedicated to the improvement of a pattern classifier generalization performances. One proposes the increasing of the training set size, by means of ,,virtual" sample generation using a set of concurrent self-organizing maps (VSG-CSOM). We have evaluated the above proposed model for facial expression recognition. One uses Japanese female facial expression (JAFFE) database corresponding to seven emotion classes: happiness, sadness, surprise, anger, disgust, fear and neutral face. We have considered the following classifiers: nearest neighbour (NN), multilayer perceptron (MLP), and radial basis function (RBF) neural classifier. One obtains an obvious improvement in generalization performances for all the considered statisticaleural classifiers. For example, the recognition score evaluated on the test set as a consequence of virtual sample generation increases for the MLP from 67.14 % to 92.86 % and for the RBF from 87.14 % to 94.29 %.
机译：本文致力于改进模式分类器的泛化性能。有人建议通过使用一组并发的自组织图（VSG-CSOM）通过“虚拟”样本生成来增加训练集的大小。我们对上述提议的面部表情识别模型进行了评估。面部表情（JAFFE）数据库对应于七个情感类别：幸福，悲伤，惊奇，愤怒，厌恶，恐惧和中性面孔。我们考虑了以下分类器：最近邻（NN），多层感知器（MLP）和径向基函数（RBF）神经分类器，对于所有考虑的统计/神经分类器，其泛化性能均得到了明显改善，例如，由于虚拟样本生成而在测试集上评估的识别分数从MLP从67.14％提高到92.86 ％和RBF从87.14％提高到94.29％。

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