特征脸方法是人脸识别领域中的一种重要方法。
Eigenface is an important approach in the field of face recognition.
结果表明，该算法的计算效率和检测精度均优于特征脸方法。
The results show that the computational efficiency and detection accuracy of the proposed algorithm is superior to Eigenfaces method.
对传统的特征脸方法进行改进，可以提高人脸正确识别率、缩短识别时间。
Modify the Eigenfaces method can improve the face correct recognition rate and reduce the recognition time.
本文根据特征脸方法的思想，提出了特征肤色的新概念，进而提出了一种新的基于特征肤色的人脸检测方法。
On the basis of eigenfaces idea, this paper proposed a new concept as eigen skin-color, and proposed a new method to detect human faces using eigen skin-color.
特征脸法是一种常用的人脸特征提取和识别方法。
Eigenfaces is one of the most popular method in face feature extraction and recognition.
本文对特征脸及其改进方法做了理论和实验比较，分析了各自的优缺点。
This paper makes comparison on theory and experimental data on Eigenfaces and its modified methods, analyses their advantages and disadvantages.
根据外耳及其所在位置的特征，提出了一种从侧脸图像上准确定位并提取出人耳的新方法。
According to the feature of ear and its position on the side face a method based on edge-tracking was adopted for human ear localization and extraction from side face images.
该文提出了一种基于二维pca的类内平均脸方法进行人脸的特征提取。
In this paper, a method based on within-class average faces of two-dimensional PCA is proposed for face feature extraction.
实验结果表明，改进后的方法对于识别多姿态人脸达到很好的效果，正确识别率比特征脸法有很大提高。
Experiments results show that the new method gains better recognition rate than eigenfaces, especially for pose varied human faces.
根据特征脸思想，提出了一种新的基于特征肤色的人脸检测方法。
On the basis of eigenfaces idea, this paper proposed a new method to detect faces using eigen skin color.
后者是利用了“特征脸”的方法，根据待识别样本到“脸空间”的距离确定它是否属于人脸，以此达到检测人脸的目的。
The PCA algorithm USES the "feature-face" approach to achieve the purpose of face detection, which determines whether it belongs to the face according to the "face space" the distance of the sample.
结果表明，新方法的识别正确率明显高于传统特征脸法。
The result shows that the new approach gets higher recognition rates than traditional one significantly.
结果表明，新方法的识别正确率明显高于传统特征脸法。
The result shows that the new approach gets higher recognition rates than traditional one significantly.
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