I was trying to build a basic Face Recognition system (PCA-Eigenfaces) using OpenCV 2.2 (from Willow Garage). I understand from many of the previous posts on Face Recognition that there is no standard open source library that can provide all the face recognition for you.
Instead, I would like to know if someone has used the functions(and integrated them):
icvCalcCovarMatrixEx_8u32fR
icvCalcEigenObjects_8u32fR
icvEigenProjection_8u32fR
et.al in the eigenobjects.cpp to form a face recognition system, because the functions seem to provide much of the required functionality along with cvSvd?
I am having a tough time trying to understand to do so since I am new to OpenCV.
Update: OpenCV 2.4.2 now comes with the very new cv::FaceRecognizer. Please see the very detailed documentation at:
http://docs.opencv.org/trunk/tutorial_face_main.html
I worked on a project with CV to recognize facial features. Most people don't understand the difference between Biometrics and Facial Recognition. There is a huge difference based on the fact that Biometrics is mainly based on histogram density matching while Facial Recognition implements this and vector support based on feature recognition from density. Check out the following link. This is the library you want to use if you are pursuing CV and Facial Recognition: www.betaface.com . Oleksander is awesome and based out of Germany, but he answers questions which is nice.
With OpenCV it's easy to get started with face detection. It comes with some predefined sets for feature detection, including face detection.
You might already know this one: OpenCV Wiki, FaceDetection
The important functions in this example are cvLoad and cvHaarDetectObjects. The first one loads the classifier and the second one applies it to an image.
The standard classifiers work pretty well. Of course you can train your own classifiers, if the standard ones don't fit your purpose.
As you said there are a lot of algorithms for face detection. Some of them might provide better results, but OpenCV is definitively a good start.
Related
My project requires face recognition based on the availability of limited features which mainly include eyes and the forehead region.
But I am unable to find any good resources to perform the same.
There are mostly resources dealing with face detection to face recognition using opencv but I have not been able to find any resource on how I can use eye detection and its feature extraction to perform face recognition with opencv.
Any guidance regarding this problem or suggestions of alternatives would be helpful.
Thank you
My idea was to use virtual mask application on basic full face image and then to make standard face_recognition / dlib's face encodings with large 68-points model for further dlib face comparation. It works. Not 100%, especially on millions-of-images data set but better then without such approach.
https://github.com/bohdansok/Face_Recognition
I am doing a project on expression recognition in opencv and have successfully extracted the face region,i am having trouble building my own algorithm for the feature points extraction of a face,can someone help me with it?
since this is my first try to answer question, I will try to do my best. I cannot post more than two links, so I will try at least to give some hints.
Your question is quite broad. It depends on the type of application and requirements. Are you doing on-line detection, is it static etc.? Based on that, you should consider keypoint detection algorithm. I do not think it is a good idea to build your own algorithm, because OpenCV already has a lot of methods that you can choose from. All you have to do in most cases is to do some pre-processing, but it depends as well.
Most popular feature detection methods are: SURF (Opencv SURF), SIFT, ORB, FAST etc. Keep in mind that SURF and SIFT ar non-free. SURF and SIFT brings a lot of features and are quite accurate, somewhat scale and rotation invariant, but also a bit slow (especially on online tracking). FAST and ORB are fast, but they are more sensitive to noise and have their own disadvantages (see descriptions on OpenCV documentation). If I were you, I would try most of them and see which one does the best job (it is not difficult to test them).
Secondly, you have to choose descriptors. Very goog introduction is here:
Descriptors tutorial. There you will find all basic info. It is important to say, that you can mix various keypoint detection algorithms and feature description algorithms (but keep in mind that not all are compatible, tutorial will explain this).
I am out of links for this post, but OpenCV documentation provides a lot of sample code for this problem, so go ahead and have a look.
Hope you succeed. Good luck.
For some time, I have been using OpenCV. It satisfied all my needs of feature extraction, matching and clustering(k-means till now) and classification(SVM). Recently, I came across Apache Mahout. But, most of the algorithms for machine learning are already available in OpenCV as well. Are there any advantages of using Mahout over OpenCV if the work relates to Videos and Images ?
This question might be put on hold since it is opinion based. I still want to add a basic comparison.
OpenCV is capable of anything about vision and ml that is possibly researched, or invented. The vision literature is based on it, and it develops according to the literature. Even the newborn ml algorithms -like TLD, originated on MATLAB- (http://www.tldvision.com/) can also be implemented using OpenCV (http://gnebehay.github.io/OpenTLD/) with some effort.
Mahout is capable, too and specific to ml. It includes not only the well known ml algorithms, but also the specific ones. Say you came across to a paper "Processing Apples with K-means Orientation Filtering". You can find OpenCV implementations of this paper all around the web. Even the actual algorithm might be open source and developed using OpenCV. With OpenCV, say it takes 500 lines of code, but with Mahout, the paper might be already implemented with a single method making everything easier
An example about this case is http://en.wikipedia.org/wiki/Canopy_clustering_algorithm, which is harder to implement using OpenCV right now.
Since you are going to work with image data sets you will need to learn about HIPI, too.
To sum up, here is a simple pro-con table:
know-how (learning curve): OpenCV is easier, since you already know about it. Mahout+HIPI will take more time.
examples: Literature + vision community commonly use OpenCV. Open source algorithms are mostly created with C++ api of OpenCV.
ml algorithms: Mahout is only about ml, whereas OpenCV is more generic. Still OpenCV has access to basic ml algorithms.
development: Mahout is easier to work with in terms of coding and time complexity (I am not sure about the latter, but I reckon it is).
I have to make an application which recognize road signs. I saw that in OpenCV folder there are some XML files for facial recognition but I do not know what that numbers in the XML represents or how they obtained those values. I need to understand this so as I can do my own XML files for road sign recognition.
I do not know much about OpenCV, anyhow I have completed my Final Year Project on Face Recognition using neural networks. Basically I used an algorithm to extract the Facial Portion from a given image. Thereafter I fed that new image (containing only the face) to a neural network that I developed using Matlab. After rigorous improvements, it was a success and by using the Simulation Feature of Matlab it was possible to precisely identify the individual.
Therefore I strongly recommend that you follow the same technique in carrying out this task.
I managed to find some interesting articles related to this topic, here, here , here and here.
What you need is two steps:
detection step
recognition step
for the detection, I suggest you to use cascade classifier that is included with opencv. It's robust and more quick than that of haar trainer. By this step you train the traffic signs to be detected. I found this tutorial that may help you how to prepare your training stuff
by this step you detect your signs . it may detect you some additional false objects in the image, for these undesired objects you can eliminate them by some processing like ratio, or color , or even by adding some negative images.
for the recognition I suggest you to use exactly the opencv's tutorial dedicated for face recognition
here you don't need a lot of modification..
I know that there are many detection techniques in OpenCV, such as SURF, STAR, ORB etc...but those techniques are for feature detection of new video feed, not for dealing with specific instances of objects that require prior learning. OpenCV's documentation isn't quite as easy to flip through and I've yet been able to find anything besides Haar, which I know deals best with face recognition.
So are there any other techniques besides Haar? The Haar technique dates back to research 10 years ago, so ideally I hope that there have been some more advances since then that have been implemented in OpenCV.
If you are looking for OpenCV machine learning type algorithms, check out this link.
For a state of the art on-the-fly object detection algorithm, have a look at OpenTLD. It uses bounding boxes and random forests to learn about an object over time. Check out the demo video here.
Also check out the matching_to_many_images.cpp sample from OpenCV. It uses feature descriptors to match objects much like Google Goggles works. A related example to this is the bagofwords_classification.cpp sample. It may be what you are looking for in this case. It uses feature detectors (SURF, SIFT, etc...) to detect objects and then classify them by comparing the relative positions of the features to a learned database of features. Have a look also at this tutorial from MIT.
The latentsvmdetect.cpp may also be a good starting point for you.
Hope that helps!