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HDG and HDGG: an extensible feature extraction descriptor for effective face and facial expressions recognition

机译：HDG和HDGG：用于有效面部和面部表达识别的可扩展功能提取描述符

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摘要

The potential of facial and facial expression recognitions has gained increased interest in social interactions and biometric identification. Earlier facial identification methods suffer from drawbacks due to the lower identification accuracy under difficult lighting conditions. This paper presents two novel new descriptors called Histogram of Directional Gradient (HDG) and Histogram of Directional Gradient Generalized (HDGG) to extracting discriminant facial expression features for better classification accuracy with good efficiency than existing classifiers. The proposed descriptors are based on the directional local gradients combined with SVM (Support Vector Machine) linear classification. To build an efficient face and facial expression recognition, features with reduced dimension are used to boost the performance of the classification. Experiments are conducted on two public-domain datasets: JAFFE for facial expression recognition and YALE for face recognition. The experiment results show the best overall accuracy of 92.12% compared to other existing works. It demonstrates a fast execution time for face recognition ranging from 0.4 to 0.7 s in all evaluated databases.
机译：面部和面部表情识别的潜力已经增加了社会互动和生物识别的兴趣。早期的面部识别方法由于困难的照明条件下的识别精度较低而受到缺点。本文呈现了两种新的新描述符，称为方向梯度（HDG）直方图和定向梯度广义（HDGG）的直方图，以提取判别面部表情特征，以提高比现有分类器良好的效率。所提出的描述符基于与SVM（支持向量机）线性分类组合的定向本地梯度。为了构建有效的面部和面部表情识别，尺寸减小的功能用于提高分类的性能。实验是在两个公共域数据集中进行的：jaffe用于面部表情识别和耶鲁面部识别。与其他现有工程相比，实验结果显示出92.12％的最佳总精度。它展示了所有评估的数据库中的面部识别的快速执行时间为0.4到0.7秒。

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