For technical details check the related medium post! Please contact if you need professional face detection & recognition project with the super high accuracy.
- Faces are Tracking, Cropping and Saving as Images from Video
- Images are Saving from Video With Appropriate Path Hierarchy
If you want to investigate some aspect of facial recognition or facial detection. One thing you are going to want is a variety of faces that you can use for your system. You can create your own face detection/recognition database by this program. face_recognizer.py recognizes the faces from video, crop and save them as an image under appropriate path hierarchy.
Once we have acquired the face data, we’ll need to read it in our program. In the demo applications I have decided to read the images from a very simple CSV file. Why? Because it’s the simplest platform-independent approach I can think of. However, if you know a simpler solution please ping me about it. Basically all the CSV file needs to contain are lines composed of a filename followed by a ; followed by the label (as integer number), making up a line like this:
/path/to/image.ext;0
You don’t really want to create the CSV file by hand. I have prepared you a little Python script create_csv.py that automatically creates you a CSV file.
face_recognizer.py calls create_csv.py and it can save the output as a csv file so you will have your own face detection/recognition dataset.
1.) Python and pip
Python is automatically installed on Ubuntu. Take a moment to confirm (by issuing a python -V command) that one of the following Python versions is already installed on your system:
- Python 2.7
- Python 3.3+
The pip or pip3 package manager is usually installed on Ubuntu. Take a moment to confirm (by issuing a pip -V or pip3 -V command) that pip or pip3 is installed. We strongly recommend version 8.1 or higher of pip or pip3. If Version 8.1 or later is not installed, issue the following command, which will either install or upgrade to the latest pip version:
$ sudo apt-get install python-pip python-dev # for Python 2.7
$ sudo apt-get install python3-pip python3-dev # for Python 3.n
2.) dlib
Install dlib prerequisites
The dlib library only has four primary prerequisites: Boost Boost.Python CMake X11/XQuartx
Installing CMake, Boost, Boost.Python, and X11 can be accomplished easily with apt-get :
$ sudo apt-get install build-essential cmake
$ sudo apt-get install libgtk-3-dev
$ sudo apt-get install libboost-all-dev
$ wget https://bootstrap.pypa.io/get-pip.py
$ sudo python get-pip.py
Install dlib with Python bindings
The dlib library doesn’t have any real Python prerequisites, but if you plan on using dlib for any type of computer vision or image processing, I would recommend installing:
- NumPy
- SciPy
- scikit-image
These packages can be installed via pip :
$ pip install numpy
$ pip install scipy
$ pip install scikit-image
Years ago, we had to compile dlib manually from source (similar to how we install OpenCV). However, we can now use pip to install dlib as well:
$ pip install dlib
3.) face_recognition 1.2.1
In this project, Adam Geitgey's face recognition system (thanks a lot to Adam) was used for face recognition and it can be installed by pypi using pip3 (or pip2 for Python 2):
pip3 install face_recognition
Just run face_recognizer.py
The default file paths are specified already, change them with yours. (edit the line 17, line 20, line 80, line 89 in face_recognizer.py)
- The sample database and dataset can be found at here
If you use this code for your publications, please cite it as:
@ONLINE{frc,
author = "Ahmet Özlü",
title = "Face Database Creator",
year = "2017",
url = "https://github.com/ahmetozlu/face_recognition_crop"
}
Ahmet Özlü
This system is available under the MIT license. See the LICENSE file for more info.