I want to detect the points on a face as shown in the picture
I am using OpenCV CascadeClassifier::detectMultiScale.
I am using the haarcascade_frontalface_alt, haarcascade_eye, haarcascade_mcs_mouth xml files.
I am satisfied with the face detection but not with the facial feature points detection.
I want the feature points detection to work for the images upto a distance of 8 feet.
I am looking for more accuracy and robustness wrt pose(15 degrees) and opening of mouth, without compromising speed.
I am looking for speed of 25fps on an i5 processor.
Can anyone suggest/refer me any libraries/open source codes for my problem.
C++ platform.
Try Flandmarks http://cmp.felk.cvut.cz/~uricamic/flandmark/
It extracts 7 feature points but you will not be able to get the feature points for upperlip and the lower lip.
You can try using an ASM mesh to fit the face. There are several implementations that use ASM/AAM.
https://code.google.com/p/asmlib-opencv/ is an open source library which has built in dataset for face images. Do look into it.
Cheers
Related
I am currently learning OpenCV API with Python and its all good. I am making decent progress. Part of it comes from Python syntax's simplicity as against using it with C++ which I haven't attempted yet. I have come to realize that I have to get dirty with C++ bindings for OpenCV at some point if I intend to do anything production quality.
Just recently I came across dlib which also claims to do all the things OpenCV does and more. Its written in C++ and offers Python API too (surprise). Can anybody vouch for dlib based on their own implementation experience?
I have used both OpenCV and dlib extensively for face detection and face recognition and dlib is much accurate as compared to OpenCV Haar based face detector. ( Note that OpenCV now has a DNN module where we get Deep Learning based Face Detector and Face Recognizer models. )
I'm in the middle of comparing the OpenCV-DNN vs Dlib for face detection / recognition. Will post the results once I'm done with it.
There are many useful functions available in dlib, but I prefer OpenCV for any other CV tasks.
EDIT : As promised, I have made a detailed comparison of OpenCV vs Dlib Face Detection methods.
Here is my conclusion :
General Case
In most applications, we won’t know the size of the face in the image before-hand. Thus, it is better to use OpenCV – DNN method as it is pretty fast and very accurate, even for small sized faces. It also detects faces at various angles. We recommend to use OpenCV-DNN in most
For medium to large image sizes
Dlib HoG is the fastest method on CPU. But it does not detect small sized faces ( < 70x70 ). So, if you know that your application will not be dealing with very small sized faces ( for example a selfie app ), then HoG based Face detector is a better option. Also, If you can use a GPU, then MMOD face detector is the best option as it is very fast on GPU and also provides detection at various angles.
For more details, you can have a look at this blog
Working on a small side project related to Computer Vision, mostly to try playing around with OpenCV. It lead me to an interesting question:
Using feature detection to find known objects in an image isn't always easy- objects are hard to find, especially if the features of the target object aren't great.
But if I could choose ahead of time what it is I'm looking for, then in theory I could generate for myself an optimal image for detection. Any quality that makes feature detection hard would be absent, and all the qualities that make it easy would exist.
I suspect this sort of thought went into things like QR codes, but with the limitations that they wanted QR codes to be simple, and small.
So my question for you: How would you generate an optimal image for later recognition by a camera? What if you already know that certain problems like skew, or partial obscuring would occur?
Thanks very much
I think you need something like AR markers.
Take a look at ArToolkit, ArToolkitPlus or Aruco libraries, they have marker generators and detectors.
And papeer about marker generation: http://www.uco.es/investiga/grupos/ava/sites/default/files/GarridoJurado2014.pdf
If you plan to use feature detection, than marker should be specific to used feature detector. Common practice for detector design is good response to "corners" or regions with high x,y gradients. Also you should note the scaling of target.
The simplest detection can be performed with BLOBS. It can be faster and more robust than feature points. For example you can detect circular blobs or rectangular.
Depending on the distance you want to see your markers from and viewing conditions/backgrounds you typically use and camera resolution/noise you should choose different images/targets. Under moderate perspective from a longer distance a color target is pretty unique, see this:
https://surf-it.soe.ucsc.edu/sites/default/files/velado_report.pdf
at close distances various bar/QR codes may be a good choice. Other than that any flat textured object will be easy to track using homography as opposed to 3D objects.
http://docs.opencv.org/trunk/doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.html
Even different views of 3d objects can be quickly learned and tracked by such systems as Predator:
https://www.youtube.com/watch?v=1GhNXHCQGsM
then comes the whole field of hardware, structured light, synchronized markers, etc, etc. Kinect, for example, uses a predefined pattern projected on the surface to do stereo. This means it recognizes and matches million of micro patterns per second creating a depth map from the matched correspondences. Note that one camera sees the pattern and while another device - a projector generates it working as a virtual camera, see
http://article.wn.com/view/2013/11/17/Apple_to_buy_PrimeSense_technology_from_the_360s_Kinect/
The quickest way to demonstrate good tracking of a standard checkerboard pattern is to use pNp function of open cv:
http://www.juergenwiki.de/work/wiki/lib/exe/fetch.php?media=public:cameracalibration_detecting_fieldcorners_of_a_chessboard.gif
this literally can be done by calling just two functions
found = findChessboardCorners(src, chessboardSize, corners, camFlags);
drawChessCornersDots(dst, chessboardSize, corners, found);
To sum up, your question is very broad and there are multiple answers and solutions. Formulate your viewing condition, camera specs, backgrounds, distances, amount of motion and perspective you expect to have indoors vs outdoors, etc. There is no such a thing as a general average case in computer vision!
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'm using OpenCV library (C++) to extract detectors from 2 images coming from a video stream taker from an aerial camera in order to, afterwards, find the matching points in successive images. i'm wondering which is the best algorithm to find robust detectors of a urban environment??
Ps. Actually I'm using SURF but when the images changes a little (because the camera is translating very slowly) the matchings between these descriptors become very few!
If you want to try different aproaches give a try to RoboRealm , they have a trial version, you just put the algoritms and seems the results, for testing purposes even if you will use OpenCV its ok.
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.