Obtaining a valid facial expression recognition (FER) method is still a research hotspot in the artificial intelligence field. In this paper, we propose a multiparameter fusion feature space and decision voting-based classification for facial expression recognition. First, the parameter of the fusion feature space is determined according to the cross-validation recognition accuracy of the Multiscale Block Local Binary Pattern Uniform Histogram (MB-LBPUH) descriptor filtering over the training samples. According to the parameters, we build various fusion feature spaces by employing multiclass linear discriminant analysis (LDA). In these spaces, fusion features composed of MB-LBPUH and Histogram of Oriented Gradient (HOG) features are used to represent different facial expressions. Finally, to resolve the inconvenient classifiable pattern problem caused by similar expression classes, a nearest neighbor-based decision voting strategy is designed to predict the classification results. In experiments with the JAFFE, CK+, and TFEID datasets, the proposed model clearly outperformed existing algorithms.

中文翻译：

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用于面部表情识别的多参数空间决策投票和融合特征


获得有效的面部表情识别（FER）方法仍然是人工智能领域的研究热点。在本文中，我们提出了一种用于面部表情识别的多参数融合特征空间和基于决策投票的分类。首先，根据多尺度块局部二值模式均匀直方图（MB-LBPUH）描述符过滤对训练样本的交叉验证识别精度来确定融合特征空间的参数。根据参数，我们采用多类线性判别分析（LDA）构建各种融合特征空间。在这些空间中，使用MB-LBPUH和定向梯度直方图（HOG）特征组成的融合特征来表示不同的面部表情。最后，为了解决相似表达类别导致的不方便分类的模式问题，设计了基于最近邻的决策投票策略来预测分类结果。在 JAFFE、CK+ 和 TFEID 数据集的实验中，所提出的模型明显优于现有算法。