I am very new in openCV, I saw it could figure out the face and return a rectangle to indicate the face. I am wondering whether there is anyway for openCV to access two images, with which contains one face, and I expect openCV to return the possibility of whether those two people are the same.
Thanks.
OpenCV does not provide a full face recognition engine.
You might want to check out this work: The One-Shot Similarity Kernel which proposes something similar to what you need. It also provides Matlab code.
Related
I would like to know more about different ways of detecting smile on image. As far as I know, there are many libraries that are allowing to detect face and smile. The ones that I've tried are:
FaceSDK from Luxand
OpenCV
OpenIMAJ
Instead of just using them, I'm curious about how they are working. I know that OpenCV and OpenIMAJ are working based on Haar classifiers. I don't really follow, how FaceSDK is doing face and smile detection though.
I can imagine, that you can get two different ways of smile detection:
Perform fully emotional detection. You can find eyes, nose, mouth and other features on face and then compute emotion based on those informations. If you get "happy" emotion, you can assume, that there is smile (or something like that, just finding mouth and checking curve on lower lip?).
Similiar to Haar cascades, search image and try to find object that is similiar to the one that you are searching (having many negative and positive samples). This one seems to be faster, but less trustworthy if not used with some "helpers".
Is there any other way? Do you guys have some articles on one of those ways?
I'm new in the texture recognition field, and I would like to know which are the possible ways to approach a texture problem in opencv.
I need to identify the texture within a region in the pic, and tell if it is uniform, homogeneous in the whole area, or not.
More in depth, I need to be able to tell if a possible fallen person is a person (with many different kind of textures) or something wrong like a pillow, or a blanket.
Could anyone suggest a solution, please?
Is there some already made opencv code to adapt?
Thanks in advance!
Why don't use haralick features? I other words they are called texture features. The base idea is to compute coocurence matrix from given gray-scaled image on base which the haralick features are computed. You can pick between different features like contrast, correlation, entropy etc. which can describe your texture. I guess for the same texture given feature should have the same (similar) value, so that might be the way for distinguishing textures.
Here some links can be helpful:
Coocurence matrix tutorial
Haralik features summary
Coocurence matrix in scikit image
So far as I know, there is no implementation of haralick features in opencv, but you can use python with scikit-image (of course you can use opencv with python if you don't mind using something different than c++).
I am very new to opencv and able to install it so far. I want to compare a face with other different faces available in library and to find out the closest match. I have tried different features but couldn't find the closer answer.
Any suggestion to choose a detector.
dimensions of input image and images in library are same.
thanks in advance
i think, you want face recognition (who is it?), not detection (is it a face?).
look here for what opencv has to offer there
I am trying to identify static hand signs. Confused with the libraries and algorithms I can use for the project.
What need to it identify hand signs and convert in to text. I managed to get the hand contour.
Can you please tell me what is the best method to classify hand signs.
Is it haar classifier, adaboost classifier, convex hull, orientation histograms, SVM, shift algorithm, or any thing else.
And also pls give me some examples as well.
I tried opencv and emugcv both for image processing. what is best c++ or c# for a real time system.
Any help is highly appreciated.
Thanks
I have implemented a handtracking for web applications in my master deggree. Basically, you should follow those steps:
1 - Detect features of skin color in a Region of Interest. Basically, put a frame in the screen and ask for the user put the hand.
2 - You should have a implementation of a lucas kanade tracker method. Basically, this alghorithm will ensure that your features are not lost through the frames.
3 - Try get more features for each 3 frames interval.
The people use many approaches, so I cannot give a unique. You could make some research using Google Scholar and use the keywords "hand sign", "recognition" and "detection".
Maybe you find some code with the help of Google. An example, the HandVu: http://www.movesinstitute.org/~kolsch/HandVu/HandVu.html
The haar classifier (method of Viola-Jones) help to detect hand, not to recognize them.
Good luck in your research!
I have made the following with OpenCV. Algorithm:
Skin detection made in HSV
Thinning (if pixel has zero neighbor than set zero)
Thicking (if pixel has neighbor nonzero then set it nonzero)
See this Wikipedia page for the details of these.
You can find the best trained cascade to detect hand using OpenCV from the GitHub...
https://github.com/Aravindlivewire/Opencv/blob/master/haarcascade/aGest.xml
Good luck...
I am currently trying to detect [P Plates]
in images made from panoramas off the top of a car (so the P plates could be coming from in front or behind me, and may be distorted). There may be more than 2 P plates so I would need the ability to detect more than 1 at a time. I have used OpenCV template matching with mixed success, it doesn't seem to cope with P plates on an angle well and I cannot seem to get it to recognise 2 in an image. I have also tried SURF but with no luck. Does anyone have any recommendations for the kind of algorithm I should use here (preferably one that is integrated into OpenCV).
You may want to go with SIFT using Rob Hess' SIFT Library. It's using OpenCV and is pretty fast.
Another way is to detect squares and than use content of the square for further processing.