CAJ | 학술논문

提出一种将加权分块图像和主成分分析（PCA ）相结合的人脸识别方法。该方法首先根据同类训练样本的平均图像与所有训练样本平均图像的距离以及类内训练样本图像与该类平均图像的距离，分别定义类间和类内图像加权函数，以获得每个训练样本图像的权重；然后将训练样本图像分块，构建所有同位置加权分块图像空间；接着基于新的样本空间对所有同位置图像分别采用PCA方法提取特征；最后用最近邻分类器实现模式分类。实验结果显示该方法较普通M PCA方法有效提高了识别率。
제출일충장가권분괴도상화주성분분석（PCA ）상결합적인검식별방법。해방법수선근거동류훈련양본적평균도상여소유훈련양본평균도상적거리이급류내훈련양본도상여해류평균도상적거리，분별정의류간화류내도상가권함수，이획득매개훈련양본도상적권중；연후장훈련양본도상분괴，구건소유동위치가권분괴도상공간；접착기우신적양본공간대소유동위치도상분별채용PCA방법제취특정；최후용최근린분류기실현모식분류。실험결과현시해방법교보통M PCA방법유효제고료식별솔。
The paper proposed a method for human face recognition combining Principal Component Analysis (PCA) and weighted block images for enhancing the differences of between-class and within-class images .According to the distances between the average images of the similar training samples and the average images of all training samples ,as well as the distances between the within-class training sample images and the average images of the same class ,the weight functions of the between-class and within-class images were defined respectively to obtain the weights of every training sample images . And the training sample images were divided into block images to construct the weighted block images space in the same position .Then PCA was used to extract features from the block images in the same position respectively based on the new sample space .Finally ,the nearest neighbor classifier was used for realizing the pattern classification .The results indicate that this method can raise the recognition rate effectively compared with ordinary MPCA method .