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Compressive sensing based facial expression recognition

机译:基于压缩感知的面部表情识别

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In this paper a new approach is proposed. The new approach is inspired by the compressive sensing theory used for data compression. Feature vectors from the facial data were extracted using compressive sensing technique. The compressive measurements were obtained at different feature vector lengths. Comparison of recognition rates with Local Binary Pattern (LBP) feature extractor were made using different classifiers. The results obtained by using three distance measures and Support Vector Machines (SVM) validate the performance of our proposed approach as it outperforms the best result obtained using LBP algorithms at different combinations of cells and bins numbers. Moreover, performance comparison using Sparse Representation Classifier (SRC) shows that LBP can only matches the proposed method performance with a very large feature vectors size which is computationally expensive for SRC.
机译:本文提出了一种新的方法。这种新方法受到用于数据压缩的压缩感测理论的启发。使用压缩感测技术从面部数据中提取特征向量。在不同特征向量长度下获得压缩测量值。使用不同的分类器对识别率与局部二进制模式(LBP)特征提取器进行了比较。通过使用三种距离测量和支持向量机(SVM)获得的结果验证了我们提出的方法的性能,因为它在不同的像元数和仓位数量组合下优于使用LBP算法获得的最佳结果。此外,使用稀疏表示分类器(SRC)进行的性能比较表明,LBP仅能以非常大的特征向量大小匹配所提出的方法性能,这对于SRC而言在计算上是昂贵的。

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