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首页> 外文期刊>Journal of Discrete Mathematical Sciences and Cryptography >Facial expression recognition using feature level fusion
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Facial expression recognition using feature level fusion

机译：使用特征级融合的面部表情识别

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摘要

In this paper, a model for facial expression recognition (FER) fusing local and global features is proposed. Local features are extracted by dividing the face into multiple regions and carefully selecting regions of interest which assists in reducing redundancy. Global features are extracted from the entire subject face and features of interest are extracted for expression recognition. The major drawback with previously available techniques in literature is that they do not take finer details into account along with global geometric features. In our work, using concatenation fusion, FER is performed in an effective way; one part of model recognizes facial expressions on a complete scale while the other performs the same task on a finer scale, thereby improving the accuracy. Accuracy with the proposed fused model is increased to 93.52% in comparison to the accuracy of 74.11% with local binary patterns and 90.64% with global features when used as standalone technique.
机译：本文提出了一种面部表情识别（FER）融合本地和全局特征的模型。 通过将面部划分为多个区域并仔细选择感兴趣的区域来提取局部特征，这有助于降低冗余。 从整个主题面部提取全局特征，提取感兴趣的特征以进行表达识别。 与以前有可用的文献技术的主要缺点是它们不会考虑更精细的细节以及全局几何特征。 在我们的工作中，使用连接融合，以有效的方式进行FER; 模型的一部分以完整的比例识别面部表情，而另一部分在更精细的规模上执行相同的任务，从而提高了准确性。 与拟议融合模型的准确性与局部二元图案的准确性增加到93.11％，局部二进制图案的准确性和90.64％用作独立技术。

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