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07 Apr 2011 Objects/Faces Detection Toolbox Objects/Faces detection using Local Binary Patterns and Haar features Author: Sebastien PARIS

nice work!!

06 Dec 2010 Objects/Faces Detection Toolbox Objects/Faces detection using Local Binary Patterns and Haar features Author: Sebastien PARIS

Hi, I confused on coventional cascade structure and multi-exit cascade structure. I need your kind to help me, I think they different like these:

Suppose that there are 10 Weaklearns: ① ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ ⑩

coventional cascade structure maybe like this:
layer-1 ①
layer-2 ② ③
layer-3 ④ ⑤ ⑥
layer-4 ⑦ ⑧ ⑨ ⑩

multi-exit cascade structure maybe like this:
layer-1 ①
layer-2 ① ② ③
layer-3 ① ② ③ ④ ⑤ ⑥
layer-4 ① ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ ⑩

This is my idea,can you agree with me ?

05 Dec 2010 Objects/Faces Detection Toolbox Objects/Faces detection using Local Binary Patterns and Haar features Author: Sebastien PARIS

Hi Sebastien,
Which face databse you have used in your code? I found you use Viola_24×24.mat and Jensen_24×24.mat,but can you tell me they are come from which face database.

27 Nov 2010 Objects/Faces Detection Toolbox Objects/Faces detection using Local Binary Patterns and Haar features Author: Sebastien PARIS

I confused on the way to select min error, i think it may be selected like this:
alpha = var1c - var2c;

beta = (m1c*var2c - m2c*var1c);

gamma = m2c*m2c*var1c - m1c*m1c*var2c + 2*var1c*var2c*log(ctep*(var2c/var1c));

std1c = 1.0/sqrt(var1c);
if(alpha==0)
{
x1=-gamma/(2*beta);
x2=x1;
std2c = std1c;
}
else
{
std2c = 1.0/sqrt(var2c);
delta = beta*beta - alpha*gamma;
if(delta==0)
{
x1=(m1c+m2c)/2;
x2=x1;
}
else
{
delta=abs(delta);
sqrtdelta = sqrt(delta);
x1 = (-beta + sqrtdelta)/(alpha);
x2 = (-beta - sqrtdelta)/(alpha);
}
}


if(m1c>m2c)
{
if(x1=x2)
{
err11= 0.5*(p2*(1.0 - erf((x1 - m2c)*std2c)) + p1*(1.0 + erf((x1 - m1c)*std1c)));
err10=err11;
Err1=err11;
Err2=Err1;
opt1=1;
opt2=opt1;
temp_th=x1;
opt=opt1;
tempmin=Err1;

}
else
{
err11= 0.5*(p2*(1.0 - erf((x1 - m2c)*std2c)) + p1*(1.0 + erf((x1 - m1c)*std1c)));
err10=err11;
Err1=err11;
opt1=1;
err21= 0.5*(p2*(1.0 - erf((x2 - m2c)*std2c)) + p1*(1.0 + erf((x2 - m1c)*std1c)));
err20= 0.5*(p2*(1.0 + erf((x2 - m2c)*std2c)) + p1*(1.0 - erf((x2 - m1c)*std1c)));
if(err21<err20)
{
Err2=err21;
opt2=1;
}
else
{
Err2=err20;
opt2=-1;
}
if(Err1<Err2)
{
tempmin=Err1;
opt=opt1;
temp_th=x1;
}
else
{
tempmin=Err2;
opt=opt2;
temp_th=x2;
}
}
}
else
{
if(x1=x2)
{
err20= 0.5*(p2*(1.0 + erf((x2 - m2c)*std2c)) + p1*(1.0 - erf((x2 - m1c)*std1c)));
err21=err20;
Err2=err20;
Err1=Err2;
opt2=-1;
opt1=opt2;
temp_th=x2;
opt=opt2;
tempmin=Err2;

}
else
{
err20= 0.5*(p2*(1.0 + erf((x2 - m2c)*std2c)) + p1*(1.0 - erf((x2 - m1c)*std1c)));
err21=err20;
Err2=err20;
opt2=-1;
err10= 0.5*(p2*(1.0 + erf((x1 - m2c)*std2c)) + p1*(1.0 - erf((x1 - m1c)*std1c)));
err11= 0.5*(p2*(1.0 - erf((x1 - m2c)*std2c)) + p1*(1.0 + erf((x1 - m1c)*std1c)));
if(err11<err10)
{
Err1=err11;
opt1=1;
}
else
{
Err1=err10;
opt1=-1;
}
if(Err1<Err2)
{
tempmin=Err1;
opt=opt1;
temp_th=x1;
}
else
{
tempmin=Err2;
opt=opt2;
temp_th=x2;
}
}
}


if(tempmin<Errormin)
{
Errormin=tempmin;
featuresIdx_opt = f;
th_opt = temp_th;
a_opt = opt;
}

27 Nov 2010 Objects/Faces Detection Toolbox Objects/Faces detection using Local Binary Patterns and Haar features Author: Sebastien PARIS

i still can't understand you method for calculating erf,can you tell me how to resolve ?

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