Is there any limit on maximum number of faces detected using DNN face detector?
By using OpenCV version 4.2.0 in c++ (VS 2019) I created project which performs face detection on the given image. I used Opencv's DNN face detector which uses res10_300x300_ssd_iter_140000_fp16.caffemodel model to detect faces.
Program works very well and detects faces as expected, but while playing and trying with different images, I come to know that for some images program does not detect any single face (even frontal faces) on the other hand If I perform dlib's HOG+SVM detector then it detects 46 faces.
Image, in which HOG+SVM detector able to detect 46 faces:
Below is the function which performs face detection using opencv and which detects 0 faces:
//variables which are used in function
const double inScaleFactor = 1.0;
const cv::Scalar meanVal = cv::Scalar(104.0, 177.0, 123.0);
const size_t inWidth = 300;
const size_t inHeight = 300;
std::vector<FaceDetectionResult> namespace_name::FaceDetection::detectFaceByOpenCVDNN(std::string filename, FaceDetectionModel model)
{
Net net;
cv::Mat frame = cv::imread(filename);
cv::Mat inputBlob;
std::vector<FaceDetectionResult> vec;
if (frame.empty())
throw std::exception("provided image file is not found or unable to open.");
int frameHeight = frame.rows;
int frameWidth = frame.cols;
if (model == FaceDetectionModel::CAFFE)
{
net = cv::dnn::readNetFromCaffe(caffeConfigFile, caffeWeightFile);
inputBlob = cv::dnn::blobFromImage(frame, inScaleFactor, cv::Size(inWidth, inHeight), meanVal, false, false);
}
else
{
net = cv::dnn::readNetFromTensorflow(tensorflowWeightFile, tensorflowConfigFile);
inputBlob = cv::dnn::blobFromImage(frame, inScaleFactor, cv::Size(inWidth, inHeight), meanVal, true, false);
}
net.setInput(inputBlob, "data");
cv::Mat detection = net.forward("detection_out");
cv::Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
for (int i = 0; i < detectionMat.rows; i++)
{
if (detectionMat.at<float>(i, 2) >= 0.5)
{
FaceDetectionResult res;
res.faceDetected = true;
res.confidence = detectionMat.at<float>(i, 2);
res.x1 = static_cast<int>(detectionMat.at<float>(i, 3) * frameWidth);
res.y1 = static_cast<int>(detectionMat.at<float>(i, 4) * frameHeight);
res.x2 = static_cast<int>(detectionMat.at<float>(i, 5) * frameWidth);
res.y2 = static_cast<int>(detectionMat.at<float>(i, 6) * frameHeight);
vec.push_back(res);
}
#ifdef aDEBUG
else
{
cout << detectionMat.at<float>(i, 2) << endl;
}
#endif
}
return vec;
}
And the original image which I am using is this.
While searching the cause behind this issue, one of my stack overflow friend asked me to crop the image and perform detection on it. After cropping image to dimension 481x576 (original was 962x576) opencv detected 18 faces. The same stack overflow friend given below expiation for why opencv detects faces after cropping and why not on full image
Below is the answer given on stackoverflow: (Link to Stackoverflow question for reference https://stackoverflow.com/questions/5...)
After a deep search, unfortunately I couldn't find a good explanation to this problem. The reason why I tried to crop image is that I assumed there can be a maximum detected face number limit. It is also not about occlusion.
I tried some image examples which includes more than 20(appx.) faces and the results were the same but when I cropped those images(decrease the number of faces), program was able to find the faces ...
