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.
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.
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.
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.
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.
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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