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assuming, you've done your PCA, and projected your data to PCA space:

 PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, _num_components);
 Mat projected = pca.project(data);

you can now setup KNearest for training:

CvKNearest knn; // assumes opencv2.4
Mat labels = ...   // nimages rows, 1 column, containing an integer per image with the resp. age group.
knn.train(projected, labels);

once this has finished, you can predict with new data. (you'll need to keep the PCA and the KNN objects):

Mat img = ... // convert to float, reshape to a single row.
Mat query = pca.project(img);
int K = 3; 
int predicted = (int) knn.find_nearest(query, K);
// now, 'predicted' holds the age-group-id, you fed into the labels when training.

assuming, you've done your PCA, and projected your data to PCA space:

 PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, _num_components);
 Mat projected = pca.project(data);

you can now setup KNearest for training:

CvKNearest knn;  // assumes opencv2.4
Mat labels = ...   // nimages rows, 1 column, containing an integer per image with the resp. age group.
knn.train(projected, labels);

once this has finished, you can predict with new data. (you'll need to keep the PCA and the KNN objects):

Mat img = ... // convert to float, reshape to a single row.
Mat query = pca.project(img);
int K = 3;     // majority vote of K neighbours
int predicted = (int) knn.find_nearest(query, K);
// now, 'predicted' holds the age-group-id, you fed into the labels when training.