最后利用加权欧氏距离分类器进行识别。
Finally, weighted Euclidean distance classifier is utilized in recognition.
最后利用加权欧氏距离分类器进行识别。
Finally, weighted Euclidean distance classifier was utilized in recognition.
最后构造了一个层次的距离分类器进行人脸的识别。
Finally, a hierarchical distance classifier is designed to recognize human faces.
提出了一种基于类内类间离散度的最小距离分类器设计方法。
Minimum distance classifier based on between-class and within-class scatter is proposed.
相似度匹配方法的研究结果表明余弦距离分类器分类效果最佳。
Studying of similarity match methods, it shows that cosine distance is best for classification.
论文提出了基于图像多路正交投影和最小距离分类器的人脸识别方法。
This paper proposes an image multi-orthogonal projection method for face recognition based on least class distance classifier.
用该方法提取纹理图像的特征，并使用加权欧式距离分类器来完成匹配工作。
We employ this method to extract the features of such texture image and use weighted Euclidean distance classifier to fulfill the matching and identification task.
为了提高最小距离分类器的分类性能，主要的改进方法是选择更有效的距离度量。
To improve its classification performance, the main methods were selecting the more effective distance measure.
在距离分类器中有多种距离度量，不同的距离度量使得分类器产生不同的输出结果。
There are several distance measurements in the distance classifier, and different distance measurement makes the classifier output different result.
结果表明，SVM分类器的识别性能优于最小欧氏距离分类器，且KDDA特征的识别性能最优。
The experimental results show that the KDDA features achieve the best recognition performance, and the SVM classifier outperforms the minimum Euclidean distance classifier.
首先基于差分图像和肤色信息检测出人脸，其次使用改进的奇异值分解方法提取面部特征，最后运用最小距离分类器进行识别。
First based on the difference image and the skin color the face is detected and localized, then features are extracted by using improved SVD, last the features is classified by minimal distance.
基于核的距离加权KNN算法解决了样本的多峰分布、边界重叠问题和分类器的精确分类决策问题。
The kernel based weighted KNN algorithm solves the multi peak distribution problem and the overlap boundary problem of the sample set, as well as the classifier's precise decision problem.
设计了一个二级组合分类器，该分类器综合使用了最小距离和BP神经网络两种模式识别方法。
A combined classifier is designed. There are two recognition ways used in the classifier. They are the least distance pattern recognition method and the BP neural network pattern recognition method.
设计了一个二级组合分类器，该分类器综合使用了最小距离和BP神经网络两种模式识别方法。
A combined classifier is designed. There are two recognition ways used in the classifier. They are the least distance pattern recognition method and the BP neural network pattern recognition method.
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