Facial expression recognition with enhanced feature extraction using graph fourier transform

Hemant Kumar Meena, K. K. Sharma, S. Joshi
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引用次数: 2

Abstract

A novel method for the facial expression recognition is proposed using the graph Fourier transform. In addition, the dimension of the feature vector is reduced by extracting the selective frequency components. The interesting observation is that the few eigenvectors corresponding to lower frequencies provide comparative accuracy in classification as using all the frequencies. Here, the spectral analysis of the facial graph signals suggests that the interrelationship of the graph signals is better captured in the lower frequencies. Experimental results on CK+ and JAFFE datasets demonstrate the effectiveness of the proposed method.
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基于图傅里叶变换的增强特征提取面部表情识别
提出了一种基于图傅里叶变换的人脸表情识别新方法。此外,通过提取选择性频率分量来降低特征向量的维数。有趣的观察是,与使用所有频率相比,与较低频率对应的少数特征向量在分类中提供了相对的准确性。在这里,对面部图形信号的频谱分析表明,图形信号的相互关系在较低的频率中被更好地捕获。在CK+和JAFFE数据集上的实验结果证明了该方法的有效性。
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