I'm looking for a solution in order to perform a morph of a given face into another.
My deal is to match their features space perfectly in order to perform a better face recognition.
SIFT/SURF did't help me, and I've tried a thin-plate spline processing from landmarks, but the resulting face is too deformed (maybe I need more points).
I'd like to obtain something like this solution, does anybody know a way to obtain those points?
I recently wrote an article on Face Morphing using OpenCV ( C++ and Python )
http://www.learnopencv.com/face-morph-using-opencv-cpp-python/
I hope you find it useful. It uses dlib for facial feature detection, followed by delaunay triangulation, and finally morphing.
Related
I am trying to do image alignment like posted on adrian blog like this image or in this link.
I want to do image alignment on this kind of image. The problem is I want to automatically detect the 4 point edges which are hard to detect in this kind of images with contour detection like in the tutorial.
Now I can do alignment just fine with manually input edge coordinates. Some of my friends suggest me to detect the edges with dlib landmark detection, but as far as I can see it mostly uses shape in which dlib automatically marking the landmark.
Do I miss something here? Or is there any tutorial or even basic guide about how to do that?
Maybe you can try to detect edges on a Gaussian pyramid. You can find an explanation here https://en.wikipedia.org/wiki/Pyramid_(image_processing). The basic idea is that by filtering with Gaussian filters of increasing size, the small objects are blurred. Thus at some scale, we get only edges of the showcase (maybe need further processing).
Here is the tutorial of opencv on image pyramid: https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_pyramids/py_pyramids.html.
I think wavelet pyramid (do wavelet transform several times) may work for your problem, since wavelet can reduce the details in image.
I need to annotate frontal (or near frontal) images using openCV. I'm currently going through the OpenCV manual and the book "Mastering OpenCV". This is the first time I'm using OpenCV and due to that I'm little bit confused with annotation and face detection.
I need to mark about 25 points in the human face. The required points are there in eyes, mouth, nose, eyes, ears .My question is :
Is it necessary to detect the face first, and then eyes, eyebrows, mouth, nose, ears. Is it the case that then only I can proceed with annotation. The reason why I'm asking this is that I'll be doing the annotation manually. So that, obviously I can see where the face is and then eyes, nose etc. I don't see the point of detecting the face first.
Can someone explain whether face detection is really needed in this case ?
According to the book "Mastering openCV" , I need to do the following step-by-step.
(1) Loading Haar Detector for face Detection
(2) Grayscale colour conversion
(3) Shrinking the image
(4) Histogram Equalization
(5) Detecting the face
(6) Face preprocessing to detect eyes, mouth, nose etc.
(7) Annotation
Face detection allows a computer algorithm to search an image much much faster for the features like eyes & mouth.
If you are annotating the image yourself then it is of course much quicker just to annotate the wanted features and ignore unwanted ones.
No, You don't need to annotate landmarks for face detection, Opencv provide you by some functions to detect faces, using some already trained models using Haar Cascades classifiers, prepared in opencv package as xml files, you just need to call them as explained here
Annotation of images by some predefined landmarks is used to detect facial expression, and some facial details as estimation of head pose in the space, for these purposes AAM, ASM models are used.
As well, annotating images is a step to train a model, for that you may use a lot of universal annotated databases, available on internet, whereas your test images don't need to be annotated
I am working on face recognition project and i am using opencv2.4.8. i am planning to implement face recognition using eigenface algorithm.
The thing is how to know the list of algorithm for face recognition that is present in opencv 2.4.8.
You can find this information in documentation
There is simple LBP based face recognition in OpenCV contrib. It should be better than PCA (eigenfaces) in terms of sensitivity to lighting conditions. I would recommend to start from this.
Have a look at OpenCV 3.0. The change list includes face recognition functionality - http://docs.opencv.org/3.0.0/dd/d65/classcv_1_1face_1_1FaceRecognizer.html#details
Also might be worth a look at another face recognition tool, openBR - http://openbiometrics.org/. You can control the keypoint detection stage too to avoid using algorithm which breach license issues.
I have been trying to do the following -
When a user uploads an Image in my web app, I'd like to detect his/her face in it and extract face (from forehead to chin and cheek to cheek) from it.
I tried OpenCV/C++ face detection using Haar Cascade but problem with it is that it gives a probability of where the face would be because of which either background of image comes inside the ROI or even the complete face doesn't come in the ROI.
I also want to detect eye inside the face and while using the above technique, the eye detection isn't that accurate.
I've read up on a new technique called Active Appearance Model (AAM). The blogs where I read up about this show that this is exactly what I want but I am lost on how to implement this.
My queries are -
Is using AAM a good idea for face detection and face feature detection.
Are there any other techniques for doing the same.
Any help on any of these is much appreciated.
Thanks !
As you noticed OpenCV's implementation of face detection is not state-of-the-art. It is a very good and robust implementation but you can do better.
Recently, Zhu and Ramanan (CVPR 2012) had intoduced Face detection, pose estimation and landmark localization in the wild which is considered to be one of the leading algorithms for face detection in recent years.
Their algorithm is capable of detecting faces both frontal and profile views AND identifying keypoints on the detected face such as eyes nose and mouth.
The authors were kind enough to publish their code along with learned models, it is a Matlab implementation but the main computations are done in C++, so it should not be too difficult to make a standalone C++ implementation of thier method.
I am trying to find a way to determine the correctness of edge detection. I want it to have little markers showing where the program determines the edges to be with something like x's or dots or lines. I am looking for something that does this: http://en.wikipedia.org/wiki/File:Corner.png
OpenCV has an edge detector and is usable in C++. As it happens the image you linked to is used in the article describing (one of) the built in algorithms.
The image you link to ins't edge detection.
Edge detection is normally just finding abrubt brightness changes in a greyscale image - you do this with differention - eg. Sobel operator.
Specifically finding corners is either done with SIFT or something like Laplacian of Gaussians
That image is not result of edge detection operations! It's corner detection. They have entirely different purposes:
Corner detection is an approach used
within computer vision systems to
extract certain kinds of features and
infer the contents of an image. Corner
detection is frequently used in motion
detection, image matching, tracking,
image mosaicing, panorama stitching,
3D modelling and object recognition.
Corner detection overlaps with the
topic of interest point detection.
OpenCV has corner detection algorithms. The latest link includes a source code example for VS 2008. You can also check this link for another example. Google can provide much more.