Thank you Aleksey

hey hi…. i’m trying to classify gender bt its not working properly…mostly it shows the csv’s file last entry as a gender..mostly.
how can i get atleast 70% accuracy. i need help plz.

/* Function For gender classification is given below*/

int gender_rec(int argc, const char *argv[])
{
// Get the path to your CSV.
string fn_csv = string(argv[2]);

// These vectors hold the images and corresponding labels.
vector images;
vector labels;

try {
read_csv(fn_csv, images, labels);
} catch (cv::Exception& e) {
cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
// nothing more we can do
exit(1);
}

if(images.size() <= 1) {
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
CV_Error(CV_StsError, error_message);
}
// Get the height from the first image. We'll need this later in code to reshape the images to their original size:
int height = images[0].rows;
// The following lines simply get the last images from your dataset and remove it from the vector. This is done,
//so that the training data (which we learn the cv::FaceRecognizer on) and the test data we test the model with, do not overlap.
Mat testSample = images[images.size() – 1];
int testLabel = labels[labels.size() – 1];
images.pop_back();
labels.pop_back();

Ptr model = createEigenFaceRecognizer();
model->train(images, labels);
const string str=”gender.xml”;
model->save(str);
//model->load(str);
int predictedLabel = model->predict(testSample);

string result_message = format(“\n\nPredicted GENDER class = %d”, predictedLabel);
cout << result_message << endl;
cout<<"Predicted Gender is ";
if(predictedLabel==0)
{
cout<<"Female"<<endl;
}
else
{
cout<<"Male"<<endl;
}

return predictedLabel;
}

In csv file i have dedicate 0 for female and 1 for male.
my image database size is 92*112 in pgm format.
please help me to increase accuracy.

Hi jedota,
inside icvGetHaarTrainingDataFromBG, icvGetBackgroundImage is called, in it, 24×24 is cropped from whole negative image.