I am a newbie in python. I am working on a real time facial expression detection project. For the first module, my aim is to detect the face.
Is there any way to detect face without using any predefined classifiers(Haar Cascade)?
Is there any algorithm to implement the Haar cascade in my own way?
Ypu can use [Haar Cascades]:http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_objdetect/py_face_detection/py_face_detection.html
Hope this helps.
Related
It is may be a very simple question. I am searching for a classifier which can be trained using C++ and OpenCV libraries for rail line detection.
Can anybody give a brief idea.
Thanks in advance.
Railway lines can be easier to detect with HOG or SIFT features, given the types of features that they contain. However, I would recommend YOLO 9000. If it already knows how to detect railway tracks, you are in luck. Otherwise, it can be trained with a collection of images.
I am new to opencv, I made some examples and I played around with it.
But I want to do a more complex project for school that includes irregular shape detection in an image.
Here a some of the shapes just to make an idea:
Can anyone give me some general guides on what are the steps for achieving this and what should I use from opencv. I want to do it in the c++ version.
Note: the image might also be colored.
You can use SURF feature of opencv to extract shape features and match with flannMatcher or BFMatcher.
Please see the examples:
1/ SURF Feature Extraction
2/ Flann Matcher
3/ BF Matcher
Hope this will help you.
I would like to identify people through face detection. I found that OpenCV implements a method to detect faces but nothing on how to recognize a specific person!
Do I have to make a learning system like SVM to be able to identify people or is there an out-of-box function for that?
Thanks,
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.
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.