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C++/CLI implementation of Face Alignment at 3000 FPS via Regressing Local Binary Features(LBF).

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skyrino

I understood this code.
double m1 = cv::mean(vec_)[0];
double m2 = cv::mean(vec_.mul(vec_))[0];
double variance = m2 - m1*m1;

But I don't understand formula below.
double variance_all = (calcVariance(delta_shapes.col(0)) + calcVariance(delta_shapes.col(1)))*N;

Does 2-D variance addition each 1-dimensional variance?
What means of N?
N is count of training sample. But I don't understand why multiply N.

Could you please provide some reference material?

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