中文摘要：

提出一种利用图像的表观特征进行头部姿态估计的方法．该方法首先使用了一维Gabor滤波器对头部图像进行特征提取，然后对提取得到的一维Gabor特征进一步使用了基于核函数的局部费舍尔判别分析方法增强特征的判别能力与传统二维Gabor特征相比，一维Gabor特征除了在计算速度和存储空间上具有明显的优势以外．更与姿态紧密相关,而基于核函数的局部费舍尔判别分析方法，能够解决姿态问题中存在的非线性问题和多模态问题．大量的实验结果表明，该算法对于姿态估计问题是有效的．特别需要指出的是，该算法具有良好的推广能力，在训练数据和测试数据异质时，该算法的性能明显高于其他对比算法的性能．

英文摘要：

This paper proposes a new pose estimation method based on the appearance of 2D head image. First, the 1D Gabor filters are used to extract the features on the raw images. Compared with the traditional 2D Gabor represents, the 1D Gabor represents are more closely related to the head pose, while the advantages of computation and storage are obvious. Second, for the extracted features, a new method, named kernel local fisher discriminant analysis, is applied to eliminate the multimodal problem, while at the same time enhance the discrimination ability. Experimental results show that the proposed method is effective for pose estimation. It must be pointed out that the generalizability of the proposed method is illustrated by the impressive performance when the training dataset and the testing dataset are heterogeneous.