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.

面部和面部表情识别的潜力已引起人们对社交互动和生物识别的兴趣。较早的面部识别方法由于在困难的照明条件下识别精度较低而具有缺点。本文提出了两个新颖的新描述符，即方向梯度直方图（HDG）和方向梯度直方图（HDGG），以提取可区分的面部表情特征，从而比现有分类器具有更好的分类精度和更高的效率。所提出的描述符基于结合SVM（支持向量机）线性分类的定向局部梯度。为了建立有效的面部和面部表情识别，使用尺寸减小的特征来提高分类的性能。实验是在两个公共领域的数据集上进行的：用于面部表情识别的JAFFE和用于面部识别的YALE。实验结果表明，与其他现有工作相比，最佳整体精度为92.12％。它显示了在所有评估数据库中人脸识别的快速执行时间为0.4到0.7 s。