Hi !

Face detected, so next step is to use some prebuild models to perform additional actions: like estimate the Age of a face, and also the Gender. In order to do this, I downloaded a couple of models from here.

Disclaimer: these models are just sample models, do not use them in production. These model does not covers all the necessary scenarios for a real implementation.

And the final winform app is kind of cute!

Below you can find the complete Form1 source code, before let’s take a look at the sample analyzing a magazine photo.

So let’s analyze the code. For this sample, we load 3 models to work with age, faces and gender.

// # detect faces, age and gender using models from https://github.com/spmallick/learnopencv/tree/08e61fe80b8c0244cc4029ac11e44cd0fbb008c3/AgeGender
const string faceProto = "models/deploy.prototxt";
const string faceModel = "models/res10_300x300_ssd_iter_140000_fp16.caffemodel";
const string ageProto = @"models/age_deploy.prototxt";
const string ageModel = @"models/age_net.caffemodel";
const string genderProto = @"models/gender_deploy.prototxt";
const string genderModel = @"models/gender_net.caffemodel";
_ageNet = CvDnn.ReadNetFromCaffe(ageProto, ageModel);
_genderNet = CvDnn.ReadNetFromCaffe(genderProto, genderModel);
_faceNet = CvDnn.ReadNetFromCaffe(faceProto, faceModel);

Once the models are loaded, in the loop to analyze camera frames, we perform face detection, and then age and gender estimation.

while (true)
{
    if (!_run) continue;
    var startTime = DateTime.Now;

    _capture.Read(_image);
    if (_image.Empty()) return;
    var imageRes = new Mat();
    Cv2.Resize(_image, imageRes, new Size(320, 240));
    var newImage = imageRes.Clone();

    if (_doFaceDetection) DetectFaces(newImage, imageRes);

    if (_fps) CalculateFps(startTime, newImage);

    var bmpWebCam = BitmapConverter.ToBitmap(imageRes);
    var bmpEffect = BitmapConverter.ToBitmap(newImage);

    pictureBoxWebCam.Image = bmpWebCam;
    pictureBoxEffect.Image = bmpEffect;
}

For each detected face, we perform the age and gender estimation. In order to do this, we crop the detected face (plus a padding), and perform the estimation on the cropped image.

private void AnalyzeAgeAndGender(int x1, int y1, int x2, int y2, Mat imageRes, Mat newImage)
{
    // get face frame
    var x = x1 - Padding;
    var y = y1 - Padding;
    var w = (x2 - x1) + Padding * 3;
    var h = (y2 - y1) + Padding * 3;
    Rect roiNew = new Rect(x, y, w, h);
    var face = imageRes[roi: roiNew];

    var meanValues = new Scalar(78.4263377603, 87.7689143744, 114.895847746);
    var blobGender = CvDnn.BlobFromImage(face, 1.0, new Size(227, 227), mean: meanValues,
        swapRB: false);
    _genderNet.SetInput(blobGender);
    var genderPreds = _genderNet.Forward();

    GetMaxClass(genderPreds, out int classId, out double classProbGender);
    var gender = _genderList[classId];

    _ageNet.SetInput(blobGender);
    var agePreds = _ageNet.Forward();
    GetMaxClass(agePreds, out int classIdAge, out double classProbAge);
    var age = _ageList[classIdAge];

    var label = $"{gender},{age}";
    Cv2.PutText(newImage, label, new Point(x1 - 10, y2 + 20), HersheyFonts.HersheyComplexSmall, 1, Scalar.Yellow, 1);
}

private void GetMaxClass(Mat probBlob, out int classId, out double classProb)
{
    // reshape the blob to 1x1000 matrix
    using var probMat = probBlob.Reshape(1, 1);
    Cv2.MinMaxLoc(probMat, out _, out classProb, out _, out var classNumber);
    classId = classNumber.X;
    Debug.WriteLine($"X: {classNumber.X} - Y: {classNumber.Y} ");
}

It’s also important to mention to the GetMaxClass() function, to retrieve the best detected element in the prob result.

And the complete source code:

References

• OpenCVSharp, https://github.com/shimat/opencvsharp
• Wikipedia, Canny Edge Detector
• OpenCV, Canny tutorial
• OpenCV, FAST tutorial
• Wikipedia, FAST
• OpenCV, Perform face detection using Haar Cascades