Faster face detection by varying detectMultiScale function parameters

In example 1, a simple face detection code is provided which runs at 1-2 frames per second.

To make it faster, we can modify the parameters to detectMultiScale function to achieve very high frame rate.
Example 2 provides the code for minor optimization of code to make it considerably reducing the size of image for detecting face and remapping the face Region Of Interest (ROI) in the original image.

Here the last two parameters - Size minSize=Size() and Size maxSize=Size() - are adaptively changed to reduce computational complexity. These parameters are responsible for restricting the search window of the function to vary from minimum size(minSize) to maximum size(maxSize) for face detection.

Note : You need  a camera connected, otherwise it will throw an error.

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Example 1: (Face detection from Video captured from camera)

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#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"

#include <iostream>
#include <stdio.h>

using namespace std;
using namespace cv;

Mat detectFace(Mat src);

int main( )
{
 VideoCapture cap(0);
 namedWindow( "window1", 1 );   

 while(1)
 {
  Mat frame;
  cap >> frame;         
  frame=detectFace(frame);
  
  imshow( "window1", frame );
  // Press 'c' to escape
  if(waitKey(1) == 'c') break;  
 }

 waitKey(0);                  
 return 0;
}

Mat detectFace(Mat image)
{
 // Load Face cascade (.xml file)
 CascadeClassifier face_cascade( "C:/OpenCV243/data/Haarcascades/haarcascade_frontalface_alt2.xml" );

 // Detect faces
 std::vector<Rect> faces;
 face_cascade.detectMultiScale( image, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );

 // Draw circles on the detected faces
 for( int i = 0; i < faces.size(); i++ )
 {
  Point center( faces[i].x + faces[i].width*0.5, faces[i].y + faces[i].height*0.5 );
  ellipse( image, center, Size( faces[i].width*0.5, faces[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
 } 
 return image;
}

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Example 2: (Optimizing the parameters of detectMultiScale for faster operation)

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#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"

#include <iostream>
#include <stdio.h>

using namespace std;
using namespace cv;

double min_face_size=20;
double max_face_size=200;

Mat detectFace(Mat src);

int main( )
{
 VideoCapture cap(0);
 namedWindow( "window1", 1 );   

 while(1)
 {
  Mat frame;
  cap >> frame;         
  frame=detectFace(frame);
  
  imshow( "window1", frame );
  // Press 'c' to escape
  if(waitKey(1) == 'c') break;  
 }

 waitKey(0);                  
 return 0;
}

Mat detectFace(Mat image)
{
 // Load Face cascade (.xml file)
 CascadeClassifier face_cascade( "C:/OpenCV243/data/Haarcascades/haarcascade_frontalface_alt2.xml" );

 // Detect faces
 std::vector<Rect> faces;
 
 face_cascade.detectMultiScale( image, faces, 1.2, 2, 0|CV_HAAR_SCALE_IMAGE, Size(min_face_size, min_face_size),Size(max_face_size, max_face_size) );
 
 // Draw circles on the detected faces
 for( int i = 0; i < faces.size(); i++ )
 { 
  min_face_size = faces[0].width*0.8;
  max_face_size = faces[0].width*1.2;
  Point center( faces[i].x + faces[i].width*0.5, faces[i].y + faces[i].height*0.5 );
  ellipse( image, center, Size( faces[i].width*0.5, faces[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
 } 
 return image;
}

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