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基于python的PCA人脸识别算法

结果如下:

具体代码见:Face_Rec.py

当每个人只选择一张图片进行训练时,训练集只有40张,所以这种情况下最多只能降到40维(即主成分为40),故为不失一般性,我只比较了降到10、20、30、40维情况下,每个人选择1-9张图片进行训练的识别准确率,结果如下: image

下面的图像更直观的表现出各维度下,选择不同照片张数的识别准确率。

image image image image image

通过上面的图像我们可以看出:

(1)在降到相同维度下(即相同主成分大小下),每个人选择的照片张数越多,即训练集越多,准确率越高。在每个人选择照片张数大于等于5时,即训练集达到整个样本数据集的一半时,准确率达到90%以上;
(2)在每个人选择照片张数相同情况下,即训练数据集大小相同情况下,降到的维数越高,即主成分越多,准确率越高;
(3)总体上来说,识别准确率整体上呈现上升趋势。随着训练数据集规模的增大而升高,同时也随着主成分大小的增大而增大。

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