opencv face recognition prediction error - c++

Question

I have stumbled upon an exception in my application that I am unable to get rid of...
I am trying to write a simple image face recognition program with all three face recognition algorithms(Eigen, Fisher and LBPH).
The unchandled exception is caused by line :
Fisher_prediction = Fisher_model->predict(crop);
and theerror message says: Unhandled exception at at 0x000007FEFDB3A49D in FaceRecognition.exe: Microsoft C++ exception: cv::Exception at memory location 0x00000000002782B0.
and is caused by: msvcr110d.dll!_CxxThrowException(void * pExceptionObject, const _s__ThrowInfo * pThrowInfo) Line 152 C++
Any sugestions where am I going wrong??
here is the rest of the code:
Mat frame = imread("1.jpg");
// Apply the classifier to the frame
if (!frame.empty()) {
cvtColor(frame, frame_gray, COLOR_BGR2GRAY);
equalizeHist(frame_gray, frame_gray);
// Detect faces
face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(30, 30));
// Set Region of Interest
cv::Rect roi_b;
cv::Rect roi_c;
size_t ic = 0; // ic is index of current element
int ac = 0; // ac is area of current element
size_t ib = 0; // ib is index of biggest element
int ab = 0; // ab is area of biggest element
// Iterate through all current elements (detected faces)
for (ic = 0; ic < faces.size(); ic++) {
roi_c.x = faces[ic].x;
roi_c.y = faces[ic].y;
roi_c.width = (faces[ic].width);
roi_c.height = (faces[ic].height);
ac = roi_c.width * roi_c.height; // Get the area of current element (detected face)
roi_b.x = faces[ib].x;
roi_b.y = faces[ib].y;
roi_b.width = (faces[ib].width);
roi_b.height = (faces[ib].height);
ab = roi_b.width * roi_b.height; // Get the area of biggest element, at beginning it is same as "current" element
if (ac > ab) {
ib = ic;
roi_b.x = faces[ib].x;
roi_b.y = faces[ib].y;
roi_b.width = (faces[ib].width);
roi_b.height = (faces[ib].height);
}
crop = frame(roi_b);
cv::resize(crop, res, Size(img_width, img_height), 0, 0, INTER_LINEAR); // This will be needed later while saving images
cvtColor(crop, gray, CV_BGR2GRAY); // Convert cropped image to Grayscale
Point pt1(faces[ic].x, faces[ic].y); // Display detected faces on main window - live stream from camera
Point pt2((faces[ic].x + faces[ic].height), (faces[ic].y + faces[ic].width));
//rectangle(frame, pt1, pt2, Scalar(0, 255, 0), 2, 8, 0);
/* Calculate the position for annotated text */
int pos_x = std::max(roi_b.tl().x - 10, 0);
int pos_y = std::max(roi_b.tl().y - 10, 0);
if(createdFisher) {
Fisher_prediction = Fisher_model->predict(crop);
QString Fisher_qs = QString::number(Fisher_prediction);
/* Create the text we will annotate the box with */
string Fisher_text = format("Prediction Fisherfaces = %d", Fisher_prediction);
putText(frame, Fisher_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0);
/* Draw a green rectangle around the detected face */
rectangle(frame, pt1, pt2, MATCH_COLOR, 1);
ui.txtConsole->appendPlainText(QString("Fisherfaces - " + Fisher_qs));
}
if(createdEigen) {
Eigen_prediction = Eigen_model->predict(crop);
QString Eigen_qs = QString::number(Eigen_prediction);
/* Create the text we will annotate the box with */
string Eigen_text = format("Prediction Eigenfaces = %d", Eigen_prediction);
putText(frame, Eigen_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0);
/* Draw a green rectangle around the detected face */
rectangle(frame, pt1, pt2, MATCH_COLOR, 1);
ui.txtConsole->appendPlainText(QString("Eigenfaces - " + Eigen_qs));
}
if(createdLBPH) {
LBPH_prediction = LBPH_model->predict(crop);
QString LBPH_qs = QString::number(LBPH_prediction);
/* Create the text we will annotate the box with */
string LBPH_text = format("Prediction LBPH = %d", LBPH_prediction);
putText(frame, LBPH_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0);
/* Draw a green rectangle around the detected face */
rectangle(frame, pt1, pt2, MATCH_COLOR, 1);
ui.txtConsole->appendPlainText(QString("Linear Binary Patern Histogram - " + LBPH_qs));
}
}
putText(frame, text, cvPoint(30, 30), FONT_HERSHEY_COMPLEX_SMALL, 0.8, cvScalar(0, 0, 255), 1, CV_AA);
imshow("original", frame);
if (!crop.empty()) {
imshow("detected", crop);
}
else
destroyWindow("detected");
}
int c = waitKey(0);
All the necessary #include and variables along with the classifier are initialised at the start of the program.

Answer 1

What I was doing wrong is that I was passing a picture to the facerecogniser that was not resized (I am using pictures that are 200x200px in my database) , so the algorithm could not do the face recognition based on a larger resolution image than the one that is in a database.