Prototype system for 3D Face Recognition built by Bas Stottelaar and Jeroen Senden for the course Introduction to Biometrics. Written in Python, applicable to the FRGC 3D data set.
The following algorithms have been implemented:
- Smoothing
- Interpolation
- Cropping
- Zooming
- Key point extraction
- Rotation
Histogram based, as proposed by Zhou et al. See http://www.3dface.org/files/papers/zhou-EG08-histogram-face-rec.pdf for more information.
- Method 0: Euclidean Distance with threshold 0.9
- Method 1: City Block Distance with threshold 0.9
- Method 2: Sample Correlation Coefficient with threshold 0.9
The following dependencies are expected to be on your system
- Python 2.7 (version 2.6 should work)
- NumPy 1.6
- SciPy 0.11
- Matplotlib 1.2
- SciKit-learn 0.12
In case of missing dependencies, they can be installed via the Python Packet Manager, via the command pip install <name>.
A few examples to get started:
python Face3D.py --enroll /path/to/abs/files --auto-id— Enrolls all files in the folder and determines person identification based on filename.python Face3D.py --authenticate /path/to/file.abs— Authenticate given file against the enrolled images. Will output the matches with scores.python Face3D.py --reevaluate --parameters N=48,K=12— Reevaluate the data set with the given parameters. Does not save data, but you could visualize something with this data.
Face3D is a commandline only application. Start a terminal and navigate to this directory where Face3D is extracted. Start the application with the command python Face3D.py.
python Face3D.py --help— Show help.python Face3D.py --parameters— Comma seperated key-value parameters for the algorithms. Defaults (and only parameters supported) areN=67,K=12.python Face3D.py --database— Specify the Face3D Database to work on. Default isdatabase.db. You need to specify this option each time if you would like to use another database for operations below.
python Face3D.py --enroll <file | directory> --person-id <id>|--auto-id— Enroll a single file or a complete directory to the Face3D Database. Multiple threads will be spawned in case of multiple files. You have to specify a person ID. In case of auto ID, it will be derrived from the*.absfilename (xxxxxd123.abs). This process can take up to 15 minutes for 350+ faces on a Intel Core i7. If a face has already been enrolled, it will notify the user. Simply delete the database file to start over.python Face3D.py --authenticate <file>— Match a given face to a face in the database.python Face3D.py --reevaluate— Reevaluate the faces with another set of parameters. Works only for feature extraction and other calculations after feature extraction. This comes in handy when evaluating different parameters.
python Face3D.py --depth-map <output.pdf> [--with-key-points]— Write a 3D depth map of enrolled faces to a PDF file, with or without key points.python Face3D.py --feature-map <output.pdf>— Write a feature map of enrolled faces to a PDF file.python Face3D.py --similarity-matrix <output.html>— Write a similarity matrix to a HTML file.python Face3D.py --roc-curve <output.pdf>— Write a ROC curve to a HTML file.
The main application logic is defined in Face3D.py. The rest of the code is stored in the folder face3d/.
One important file is face3d/algorithms.py. Here are all the algorithms programmed that are used for smoothing, interpolating, finding key points, cropping, feature extracting. Dependencies are face3d/absfile.py and face3d/face.py. The first reads *.abs files into memory and the second one is a wrapper for the data and handles views and compression.
On of the two files left is face3d/database.py, a wrapper for an SQLite3 database file. It reads and writes faces and features. Last but not least is face3d/utils.py as a place for common used methods.
See the LICENCE file.