问题
I am developing one security related project, there is need to check any face is detected or not, if face is detected then do some action, if face is not detected then close app.
Everything is perfect working, i am using SurfaceView which is implemented SurfaceHolder.Callback and in that open camera and camera have one method name is startFaceDetection using this method i detect face.
code for reference
public class SurfaceViewPreview extends SurfaceView implements SurfaceHolder.Callback {
private SurfaceHolder mHolder;
private Camera mCamera;
public SurfaceViewPreview(Context context, AttributeSet attrs) {
super(context, attrs);
setWillNotDraw(false);
mHolder = getHolder();
mHolder.addCallback(this);
mHolder.setType(SurfaceHolder.SURFACE_TYPE_PUSH_BUFFERS);
}
public void surfaceCreated(SurfaceHolder holder) {
try {
if (Camera.getNumberOfCameras() <= 0 || ContextCompat.checkSelfPermission(getContext(), Manifest.permission.WRITE_EXTERNAL_STORAGE)
!= PackageManager.PERMISSION_GRANTED)
return;
mCamera = Camera.open(0);
mCamera.setPreviewDisplay(mHolder);
} catch (Exception e) {
e.printStackTrace();
if (this.mCamera != null) {
this.mCamera.release();
this.mCamera = null;
}
}
}
public void surfaceDestroyed(SurfaceHolder holder) {
if (Camera.getNumberOfCameras() <= 0 || ContextCompat.checkSelfPermission(getContext(), Manifest.permission.WRITE_EXTERNAL_STORAGE)
!= PackageManager.PERMISSION_GRANTED)
return;
mCamera.stopPreview();
mCamera.release();
mCamera = null;
}
public void surfaceChanged(SurfaceHolder holder, int format, int w, int h) {
if (Camera.getNumberOfCameras() <= 0 || ContextCompat.checkSelfPermission(getContext(), Manifest.permission.WRITE_EXTERNAL_STORAGE)
!= PackageManager.PERMISSION_GRANTED)
return;
mCamera.startPreview();
mCamera.setFaceDetectionListener(new Camera.FaceDetectionListener() {
@Override
public void onFaceDetection(Camera.Face[] faces, Camera camera) {
// face is detected.
}
});
mCamera.startFaceDetection();
}
}
Now, problem if any human post if i shown to camera then detected as human, but i want real human face detection not fake poster face.
Possible way to handle my requirement.
1) Capture 10 images periodically and check all variation is same then it means static face is there (like poster which is mounted in wall).
2) Write any proper algorithm which tell to detected face is real human or fake face.
3) Any library is available which is said human face is really available or not.
if anyone have idea please suggest, how to solve above issue (any code is available then share with me), response is appreciated !
how can use adapting learning ways to conclude real vs fake picture/video frame.
回答1:
You could use the parallax effect. First you take a 2 pictures from 2 different locations which are like 2cm apart. then you could compare the images and see:
*If they are very similar(almost same) then the image is 2d and it is a poster
*If they are very different then it is a 3d Face
Another way you could do this is by using the camera flash. The flash would cause a bit of reflection on photographs and this would prevent people from using a video to bypass your system as a screen would cause a lot of glare would would block the face preventing the camera from detecting the face. All you would need to do is add a flash(preferably blinking at like 100Hz so the people can't see it but it would show up in a picture)
I hope this helped :)
回答2:
I had a challenge solving problems similiar the one @YogeshRathi. I had an algorithm with the CV2 library (Python) recognizing faces taken from a security camera. Every 5 seconds I took pictures, and the algorithm recognized faces in the poster hanged on the wall.
After test different solutions (other algorithms, train models...) what I did finally was generate a buffer, in which there were 5 pictures always, one in and another out. The one entering to the buffer do it with the list of coordinates of all rectangles that contains a face (5 faces in the pictures --> 5 rectangles), and compare with the rest of the pictures inside the buffer. The comparison of pictures consists in compare the rectangles (every rectangle has 4 coordinates) between both pictures, by the substraction of every single coordinate. If the rectangle is static (a face in a poster have almost the same rectangle in different pictures) the difference between both rectangles is simbolic, so then, unless they have different number of rectangles, if all the rectangles in both pictures have simbolic differences they are similar.
If in a picture appears a real person we will have different number of rectangles (the number of faces inside the poster and the one that belongs to the real person), o at least one of them will be different to the list of rectangles of the picture is being compared with. If the rectangles in both pictures are similar, I put a flag in a Historial field, that is 0. If there are different rectangles, the flag is 1.
You compare the picture that enters the buffer one to one to the rest of the pictures in the buffer. So when you finish, you have a list of flags (like this [0,0,0,1,1]) attached to every picture.
When the picture go out of the buffer you evaluate the Historial field. If a 0 is contained in the list it means that at least there are a picture that is identical, so then you can consider there are no faces on it you have to identify, because all it contains only fake faces from a poster.
来源：https://stackoverflow.com/questions/38997240/how-to-find-out-detected-face-is-real-or-fake