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jakewilliami / FaceDetection.jl

A face detection algorithm using Viola-Jones' rapid object detection framework written in Julia
MIT License
28 stars 2 forks source link
algorithm detection-algorithm face-detection facedetection faces haar julia julia-language julia-package julialang viola-jones

readme

FaceDetection using Viola-Jones' Robust Algorithm for Object Detection

Dev CI Code Style: Blue Project Status

Caveats;— help wanted

Currently, there are two main areas I would love some help with:

  1. Reading from pre-trained data files would greatly help those who are not training the object detection framework themselves; and
  2. Creating (scalable) bounding boxes around detected objects. Everyone loves a visual; you can show your grandmother a face with a box around it, but she'll switch off if you show her any code. People want pictures!

Since this project helped me with the get_faceness function (this idea of scoring the "faceness" of an image), now that I have data from this I have put this project on the back burner. However, in the interest of others, if anyone were to help with these two items (and any other issues), they would forever have my gratitude.

Introduction

This is a Julia implementation of Viola-Jones' Object Detection algorithm. Although there is an OpenCV port in Julia, it seems to be ill-maintained. As this algorithm was created for commercial use, there seem to be few widely-used or well-documented implementations of it on GitHub. The implementation this repository is based off is Simon Hohberg's Pythonic repository, as it seems to be well written (and the most starred Python implementation on GitHub, though this is not necessarily a good measure). Julia and Python alike are easy to read and write in — my thinking was that this would be easy enough to replicate in Julia, except for Pythonic classes, where I would have to use structs (or at least easier to replicate from than, for example, C++ or JS — two other highly-starred repositories.).

I implore collaboration. I am an undergraduate student with no formal education in computer science (or computer vision of any form for that matter); I am certain this code can be refined/optimised by better programmers than myself. Please, help me out if you like!

How it works

In an over-simplified manner, the Viola-Jones algorithm has some four stages:

  1. Takes an image, converts it into an array of intensity values (i.e., in grey-scale), and constructs an Integral Image, such that for every element in the array, the Integral Image element is the sum of all elements above and to the left of it. This makes calculations easier for step 2.
  2. Finds Haar-like Features from Integral Image.
  3. There is now a training phase using sets of faces and non-faces. This phase uses something called Adaboost (short for Adaptive Boosting). Boosting is one method of Ensemble Learning. There are other Ensemble Learning methods like Bagging, Stacking, &c.. The differences between Bagging, Boosting, Stacking are:
    • Bagging uses equal weight voting. Trains each model with a random drawn subset of training set.
    • Boosting trains each new model instance to emphasize the training instances that previous models mis-classified. Has better accuracy comparing to bagging, but also tends to overfit.
    • Stacking trains a learning algorithm to combine the predictions of several other learning algorithms. Despite this method being developed at the start of the century, it is blazingly fast compared to some machine learning algorithms, and still widely used.
  4. Finally, this algorithm uses Cascading Classifiers to identify faces. (See page 12 of the original paper for the specific cascade).

For a better explanation, read the paper from 2001, or see the Wikipedia page on this algorithm.

Quick Start

using FaceDetection

# Constants
pos_training_path, neg_training_path = "...", "..."
num_faces, num_non_faces = length(filtered_ls(pos_testing_path)), length(filtered_ls(neg_testing_path))  # You can also just put in a simple number here, if you know how many training images you have
num_classifiers = 10
min_feature_height, max_feature_height = 0, 19
min_feature_width, max_feature_width = 0, 19
scale, scale_to = true, (19, 19)  # we want all training images to be standardised to size 19x19

# Train a model
classifiers = FaceDetection.learn(pos_training_path, neg_training_path, num_classifiers, min_feature_height, max_feature_height, min_feature_width, max_feature_width; scale = scale, scale_to = scale_to)

# Results
correct_faces = sum(ensemble_vote_all(pos_testing_path, classifiers, scale=scale, scale_to=scale_to))
correct_non_faces = num_non_faces - sum(ensemble_vote_all(neg_testing_path, classifiers, scale=scale, scale_to=scale_to))
println((correct_faces / num_faces) * 100, "% of faces were recognised as faces")
println((correct_non_faces / num_non_faces) * 100, "% of non-faces were identified as non-faces")

For more examples like this, see examples/.

Citation

If your research depends on FaceDetection.jl, please consider giving us a formal citation: citation.bib.

Miscellaneous Notes

Timeline of Progression

Acknowledgements

Thank you to:

A Note on running on BSD:

The default JuliaPlots backend GR does not provide binaries for FreeBSD. Here's how you can build it from source.. That said, StatsPlots is only a dependency for an example, and not for the main package.