Emotion detection from facial expression has been well studied. There are numerous techniques has been discussed for the accuracy of emotion detection, however the methods suffer with higher false classification ratio. Towards the development of emotion detection, a novel region based multi feature similarity approach has been presented in this article. Considering, shape and geometry measure alone would not acquire higher performance in the classification. It is necessary to consider and combine multiple features towards the problem. With this motivation, the proposed Regional Multi Feature Similarity (RMFS) based emotion detection algorithm enhances the input facial image and extracts shape feature, geometry feature and wrinkle features with colors are considered. Extracted features are trained with neural network. At the classification stage, MFS measure has been estimated towards the features of various emotion class in different layers of neural network. Finally, a single one has been classified as result using artificial neural network. The proposed method improves the performance of emotion detection with reduced false ratio.

从面部表情进行情感检测已经得到了很好的研究。已经讨论了许多用于情感检测的准确性的技术，但是这些方法具有较高的错误分类率。面向情感检测的发展，本文提出了一种基于区域的新型多特征相似性方法。考虑到单独使用形状和几何尺寸无法在分类中获得更高的性能。有必要考虑并组合针对该问题的多个功能。以此动机为基础，提出的基于区域多特征相似度（RMFS）的情感检测算法增强了输入的面部图像，并考虑了具有颜色的形状特征，几何特征和皱纹特征。使用神经网络训练提取的特征。在分类阶段，已针对神经网络不同层中各种情感类别的特征对MFS度量进行了估计。最后，使用人工神经网络将一个分类为结果。所提出的方法提高了情感检测的性能，降低了错误率。