This is an implement of Viola-Jones Detection Framework and is used in human face detection.
This implement of Viola-Jones Framework require python version 3.5.2, and depends on the following modules:
| module | version | comment |
|---|---|---|
| numpy | 1.13.3 | |
| scipy | 1.0.0 | |
| opencv-python | 3.4.0.14 | for capturing image |
| scikit-learn | 0.19.1 | for shuffling data |
Run the following command to begin face detection:
python detect.pyHaar-like features are proposed by Viola and Jones, adapting the idea (from Papageoriou et al) of using Haar wavelets.
In this implement, five types of Haar-like features are used. They are: left-right, top-bottom, horizontal-middle, vertical-middle, diagonal.
To speed up features extraction process, an intermediate representation for the image called integral image is used.
AdaBoost is short for Adaptive Boosting, which is a kind of method of ensemble learning.
For more info about AdaBoost, refers to GitHub - Donny-Hikari/AdaBoost
To speed up detection process, as well as to decrease the false positive rate while sustaining a high detection rate (i.e. to improve precision), Viola and Jones invent the boosted cascade. That is, to cascade multiple AdaBoost classifiers.
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Yoav Freund; Robert E. Schapire. AT&T Labs – Research, Shannon Laboratory. Journal of Japanese Society for Artificial Intelligence,14(5):771-780, September 1999. A Short Introduction to Boosting.
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Paul Viola; Michael J. Jones. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, May 2004. Rapid Object Detection Using a Boosted Cascade of Simple Features.
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Paul Viola; Michael J. Jones. International Journal of Computer Vision 57(2), 137–154, 2004. Robust Real-Time Face Detection.
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| Donny Hikari |