Maximum distance a face can be detected (from camera)?

Facedetection

asked 2015-02-25 23:15:20 -0600

safrimus
1 ●1

Hello,

I am currently working on a project in which I am trying to detect a person face using OpenCV. I am able to detect the face successfully using the detectMultiScale function as long as the person is less than about 3.5 meters from the camera. Since I am new to OpenCV, I wanted to know if there was a way to increase the distance at which the user can stand? or is this a limitation of the lbpcascade_frontalface.xml file?

Thanks in advance!

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there is a minimum Size limit of 24x24 pixels.

(you can't detect something smaller than the cascades were trained upon.)

berak ( 2015-02-26 01:11:51 -0600 )edit

... but you can train a cascade with smaller data

thdrksdfthmn ( 2015-02-26 02:24:14 -0600 )edit

^^ probably not that much. 20 seems to be lower limit

berak ( 2015-02-26 02:26:35 -0600 )edit

well I've trained an 14x18 LBP, and it worked pretty nice, ofcourse with a svm classifier

thdrksdfthmn ( 2015-02-26 05:55:03 -0600 )edit

oooh, so, that's no more a Cascade or such, just a 2-class SVM ? for faces ? using integral images, too ? sounds pretty interesting !

berak ( 2015-02-26 06:01:37 -0600 )edit

no-no-no, I have trained an LBP cascade for faces, but because there were many false-detections, I have also trained a classifier for improve the detection.

thdrksdfthmn ( 2015-02-26 07:04:51 -0600 )edit

That makes sense. Thanks for your replies. I might train on smaller data or just use some other feature to detect the user.

safrimus ( 2015-02-26 09:58:47 -0600 )edit

do not go very far in the size, you'll not get anymore the faces centered or you'll going to loose information

thdrksdfthmn ( 2015-02-26 10:15:18 -0600 )edit

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answered 2015-02-27 09:50:51 -0600

jwcrawley

26 ●2 http://crankylinuxuser...

It's what I'm currently doing with a project now. Your goal is to find the smallest size of an image that a face can be detected from. You've trained an LBP to detect faces, but are receiving false positives.

You can constrain the positives by using another cascade to detect for eyes. For example, you find 5 faces in an image 1024x768. 4 are real people, while one is the cascade giving a false positive (on a wall or something).

By using the eye-detection cascade, you can feed the detected faces into the eye detector. Do this with the image(cv::Rect eyes), where image is where the webcam image is stored. If then, you watch for the size of the vector<cv::rect> where the eyes are stored, you can tell if it's successful or not. If it's a null vector, it's probably ... (more)

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I think that training a classifier for validating the faces is much faster than detecting the eyes...

thdrksdfthmn ( 2015-03-02 03:38:19 -0600 )edit

That is true. However the LBP does tend to give false positives. Since it does, you can run the faces directly through the Haar cascade that detects eyes. That is really quick. And then, if you don't detect eyes for a given face, you can throw the face out.

My way was a compromise of getting the speed of the face LBP, and the accuracy of the face Haar cascade. It's a bit more complicated, but does work.

jwcrawley ( 2015-03-03 06:51:08 -0600 )edit

your approach is good, but it has nothing to do with the real question: "Maximum distance a face can be detected (from camera)?" which is linked to the size of the training data...

thdrksdfthmn ( 2015-03-03 07:28:38 -0600 )edit

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