As Gilles says, the scanning windows approach is pretty common for object
(and face) detection. Have you looked at the Viola Jones paper? It's the
standard for face detection and now that we have adaboost classifiers you
should be able to knock up an example quite quickly. Scikit Image might be
quite useful for you as well, efficient versions of the integral image
calculations are already implemented.
Brian
On Mar 19, 2013 7:50 AM, "Gilles Louppe" <g.lou...@gmail.com> wrote:
> Hi,
>
> Short answer: you cant.
>
> Longer answer: If you use as training samples the whole images (with faces
> somewhere in there), then your model is learning to discriminate between
> your 2 categories, from the whole images, with **no** information about
> where the faces are actually located. As such, it is not learning to detect
> faces. It is learning to discriminate between images that contain faces
> (somewhere) and images that do not. What I mean to say is that it may as
> well learn to detect "non-faces" elements (background stuff) than to detect
> faces. Therefore, it may predict than an image is a "face" because it
> contains no "non-faces" elements. It is also very like to exploit artifacts
> to "learn" to discriminate between your categories (e.g., say that all
> images with faces contain a blue sky, then a new image may be labeled to be
> a face because it contains a blue sky, not because it contains a face).
>
> If you want to build a proper face detector, than you should train your
> model directly on the regions of interest containing the faces. Once your
> model is trained, then to detect faces in new image, you can scan the whole
> image with a sliding window of the size of training rectangles and apply
> your detector on each one of these.
>
> Hope this helps,
>
> Gilles
>
>
> On 19 March 2013 05:19, Fimi <afrim...@yahoo.com> wrote:
>
>> Hello,
>> **
>> I've got non linear multiclass classification for support vector
>> machines to work and it does predict the correct face and non face images.
>> It has been a very steep learning curve for me because this is the first
>> time I do this type of work.
>> **
>> I would like to see if you can guide me on a good direction on how I
>> would go about finding the area of the face is. My goal is to draw a square
>> around the area of a face so that I can visually show where it has found it.
>> **
>> I have spent many hours (and days in this project) on the website of
>> scikit-learn page trying to find out things. When I do a prediction, the
>> only thing i get back is a list of predicted labels. I also looked into the
>> values and functions that are present in the object when performing the
>> SVC.fit(X,y) and SCV.predict(..) but could not figure out how to do this.
>> **
>> I would appreciate any help. Thank you.
>> **
>> Here's some code snippet that i currently have:
>> parameters = {'kernel':('linear','rbf'), 'C':[1,2,3,4,5, 10]}
>> svr = SVC(verbose=True)
>> clf = GridSearchCV(svr, parameters)
>> clf.fit(data, y_train)
>> print "Best Estimator",clf.best_estimator_
>>
>> y_predict = clf.predict(test_data)
>> #plot one of the images that was predicted
>> #draw squares to indicate location of faces
>> **
>> **
>> **
>>
>>
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