https://github.com/donny-hikari/viola-jones

A face detection program in python using Viola-Jones algorithm.
https://github.com/donny-hikari/viola-jones

adaboost boosting computer-vision emsembling face-detection haar-cascade machine-learning viola-jones

Last synced: 7 months ago
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A face detection program in python using Viola-Jones algorithm.

• Host: GitHub
• URL: https://github.com/donny-hikari/viola-jones
• Owner: Donny-Hikari
• Created: 2018-04-08T17:57:17.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-06-24T13:22:22.000Z (over 7 years ago)
• Last Synced: 2025-03-26T07:11:10.963Z (8 months ago)
• Topics: adaboost, boosting, computer-vision, emsembling, face-detection, haar-cascade, machine-learning, viola-jones
• Language: Python
• Size: 224 KB
• Stars: 49
• Watchers: 4
• Forks: 23
• Open Issues: 10
• Metadata Files:
  • Readme: README.md

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README

          

# Viola-Jones Detection Framework

This is an implement of Viola-Jones Detection Framework and is used in human face detection.

## Requirements

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

## Usage

Run the following command to begin face detection:

```bash

python detect.py

```

## Main Concepts

### Haar-like Features

Haar-like features are proposed by Viola and Jones, adapting the idea (from Papageoriou et al) of using Haar wavelets.

![Haar-like Features proposed by Viola and Jones](./docs/haar-like.png)

In this implement, five types of Haar-like features are used. They are: left-right, top-bottom, horizontal-middle, vertical-middle, diagonal.

![Haar-like Features in this implement](./docs/My-Haar-like-Features.png)

### Integral Image

To speed up features extraction process, an intermediate representation for the image called integral image is used.

![Integral Image](./docs/integral-image.png)

### AdaBoost

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](https://github.com/Donny-Hikari/AdaBoost)

### BoostedCascade

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.

![The Attentional Cascade](./docs/attentional-cascade.png)

## References

1. 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.

2. 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.

3. Paul Viola; Michael J. Jones. International Journal of Computer Vision 57(2), 137–154, 2004. Robust Real-Time Face Detection. 

## Author

||

|:--:|

| [![Donny Hikari](https://avatars3.githubusercontent.com/u/22200374?s=128)](https://github.com/Donny-Hikari) |

| [Donny Hikari](https://github.com/Donny-Hikari) |