https://github.com/maettuu/thesis-on-low-resolution-face-recognition
Repository for Bachelor's Thesis 2022
https://github.com/maettuu/thesis-on-low-resolution-face-recognition
bachelors-thesis bob distance-measures face-recognition machine-learning python rank-lists scface
Last synced: 8 months ago
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Repository for Bachelor's Thesis 2022
- Host: GitHub
- URL: https://github.com/maettuu/thesis-on-low-resolution-face-recognition
- Owner: maettuu
- Created: 2022-07-06T12:12:02.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-12-13T09:34:10.000Z (10 months ago)
- Last Synced: 2025-01-05T02:14:54.092Z (9 months ago)
- Topics: bachelors-thesis, bob, distance-measures, face-recognition, machine-learning, python, rank-lists, scface
- Language: Python
- Homepage:
- Size: 4.46 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Bachelor's Thesis: Low-Resolution Face Recognition using Rank Lists
This repository serves as code base for the corresponding [bachelor's thesis](https://capuana.ifi.uzh.ch/publications/PDFs/22681_1_Bachelor_Thesis_Matthias_Mylaeus.pdf). The code is written in `Python v3.7` and uses [`bob.bio.face`](https://www.idiap.ch/software/bob/).
Main packages: `bob` : {`bio.base`, `bio.face`, `pipelines`}, `sklearn` : {`pipeline`}, `numpy`, `scipy`, `argpars`, `pandas`
## Installation / Virtual Environment (Linux)
1. First, install [mambaforge](https://github.com/conda-forge/miniforge#mambaforge).
2. Configure the conda installation by executing the following commands:
```
conda install -n base -c conda-forge mamba
mamba update -n base -c conda-forge conda mamba
conda config --set show_channel_urls True
```
3. Create a virtual environment using `mamba` as follows:
```
mamba create --name bob_env --override-channels -c https://www.idiap.ch/software/bob/conda -c conda-forge python=3 bob.io.base bob.bio.face pytorch
```
3. Activate the environment by `conda activate bob_env` and configure its channels as follows:
```
conda config --env --add channels conda-forge
conda config --env --add channels https://www.idiap.ch/software/bob/conda
```
4. Install packages by using `mamba install `.
5. [Download](https://www.scface.org/) and save the SCface database.
6. In the activated environment, configure Bob with the path to the database directory as follows:
```
bob config set bob.bio.face.scface.directory
```
7. The configuration can be checked using:
```
bob config show
```
## Pipeline Setup
To successfully install the required packages for running the `simple_pipe.py`, follow these steps:
1. Install buildout.cfg `pip install zc.buildout`.
2. Run `buildout`. This should create a couple of folders and files.
3. Whenever `bob` or `python` is called through the terminal, use `bin/bob` and `bin/python` instead.
## Feature Extraction
Install all necessary packages for `simple_pipe.py` and run the following command three times using `close`, `medium`, and `far` each once as protocol.
```
bin/bob bio pipelines vanilla-biometrics scface- ./helpers/simple_pipe.py -vvv -o samples_pipe_all -c --group eval
```
This runs the pipeline and saves all checkpoint data in a folder called `samples_pipe_all`. The extracted features used for this project can be found in the subdirectory called `samplewrapper-2`. To read the files, use the terminal with the command `h5dump -y .h5`
## Running the Code
Use the terminal for starting the script with the command `bin/python main.py`. The following options are available:
* --protocol, -p\
Choose between the `close`, `medium`, or `far` protocol used for the comparison (default: `all`)
* --comparison_method, -c\
Specify a comparison method (default: `all`)
| Approach | Comparison Methods |
|----------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|
| direct comparison | `baseline` |
| rank list comparison (use `rank_list_comparison` to run all methods) | `mueller2010`, `schroff`, `mueller2013`, `wartmann`, `spearman`, `kendall`, `weighted_kendall` |
| standardization comparison (use `standardization_comparison` to run all methods) | `cosine`, `braycurtis`, `canberra`, `cityblock`, `sqeuclidean`, `minkowski` |
* --standardization_method, -s\
Select `standardize`, `subtract_mean`, or `omitted` to define the preprocessing of the lists with cosine distances. This argument is only important whenever methods for a standardization comparison are chosen (default: `standardize`).
* --record_output, -r\
Include to record scores such as recognition rates, score files, preprocessing time, and runtime (default: `False`)
* --enable_larger_cohort, -lc\
Include to extend the cohort with 43 samples (default: `False`)
## Evaluation of Results
If the recording of scores is enabled, an `output` directory is created in which there are `.csv` files for every comparison method and protocol used for verification, as well as a `.csv` file containing the recognition rates used for identification.
To evaluate the verification results, use the following command, including the files for visualizing:
```
bin/bob bio roc -v -o .pdf -ts "" -lg
```
The repository additionally includes an `example_output` directory which includes plots and recorded output. Furthermore, the `plots` repository includes python executables for the comparison methods `wartmann` and `minkowski`, which generate plots with values computed with various parameters.