{"id":20238319,"url":"https://github.com/basilfx/course-face3d","last_synced_at":"2025-04-10T19:34:01.151Z","repository":{"id":5883629,"uuid":"7101449","full_name":"basilfx/Course-Face3D","owner":"basilfx","description":"Implementation of a prototype 3D Face recognition system","archived":false,"fork":false,"pushed_at":"2012-12-10T21:52:38.000Z","size":108,"stargazers_count":22,"open_issues_count":0,"forks_count":9,"subscribers_count":8,"default_branch":"master","last_synced_at":"2025-03-24T17:12:14.563Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/basilfx.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2012-12-10T21:52:27.000Z","updated_at":"2021-09-23T02:36:26.000Z","dependencies_parsed_at":"2022-09-05T17:10:40.903Z","dependency_job_id":null,"html_url":"https://github.com/basilfx/Course-Face3D","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/basilfx%2FCourse-Face3D","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/basilfx%2FCourse-Face3D/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/basilfx%2FCourse-Face3D/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/basilfx%2FCourse-Face3D/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/basilfx","download_url":"https://codeload.github.com/basilfx/Course-Face3D/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248281422,"owners_count":21077423,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-14T08:33:14.976Z","updated_at":"2025-04-10T19:34:01.123Z","avatar_url":"https://github.com/basilfx.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Face3D\nPrototype 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.\n\n## Features\nThe following algorithms have been implemented:\n\n### Normalization\n* Smoothing\n* Interpolation\n* Cropping\n* Zooming\n* Key point extraction\n* Rotation\n\n### Feature extraction\nHistogram based, as proposed by Zhou et al. See http://www.3dface.org/files/papers/zhou-EG08-histogram-face-rec.pdf for more information.\n\n### Distance Metrics\n* Method 0: Euclidean Distance with threshold 0.9\n* Method 1: City Block Distance with threshold 0.9\n* Method 2: Sample Correlation Coefficient with threshold 0.9\n\n## Installation\nThe following dependencies are expected to be on your system\n\n* Python 2.7 (version 2.6 should work)\n* NumPy 1.6\n* SciPy 0.11\n* Matplotlib 1.2\n* SciKit-learn 0.12\n\nIn case of missing dependencies, they can be installed via the Python Packet Manager, via the command `pip install \u003cname\u003e`. \n\n## Quick Start\nA few examples to get started:\n\n* `python Face3D.py --enroll /path/to/abs/files --auto-id` \u0026mdash; Enrolls all files in the folder and determines person identification based on filename.\n* `python Face3D.py --authenticate /path/to/file.abs` \u0026mdash; Authenticate given file against the enrolled images. Will output the matches with scores.\n* `python Face3D.py --reevaluate --parameters N=48,K=12` \u0026mdash; Reevaluate the data set with the given parameters. Does not save data, but you could visualize something with this data.\n\n## Usage\nFace3D 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`.\n\n### General\n* `python Face3D.py --help` \u0026mdash; Show help.\n* `python Face3D.py --parameters` \u0026mdash; Comma seperated key-value parameters for the algorithms. Defaults (and only parameters supported) are `N=67,K=12`.\n* `python Face3D.py --database` \u0026mdash; Specify the Face3D Database to work on. Default is `database.db`. You need to specify this option each time if you would like to use another database for operations below.\n\n### Face management\n* `python Face3D.py --enroll \u003cfile | directory\u003e --person-id \u003cid\u003e|--auto-id` \u0026mdash; 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 `*.abs` filename (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.\n* `python Face3D.py --authenticate \u003cfile\u003e` \u0026mdash; Match a given face to a face in the database.\n* `python Face3D.py --reevaluate` \u0026mdash; 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.\n\n### Visualization \u0026 Statistics\n* `python Face3D.py --depth-map \u003coutput.pdf\u003e [--with-key-points]` \u0026mdash; Write a 3D depth map of enrolled faces to a PDF file, with or without key points.\n* `python Face3D.py --feature-map \u003coutput.pdf\u003e` \u0026mdash; Write a feature map of enrolled faces to a PDF file. \n* `python Face3D.py --similarity-matrix \u003coutput.html\u003e` \u0026mdash; Write a similarity matrix to a HTML file.\n* `python Face3D.py --roc-curve \u003coutput.pdf\u003e` \u0026mdash; Write a ROC curve to a HTML file.\n\n## Source code\nThe main application logic is defined in `Face3D.py`. The rest of the code is stored in the folder `face3d/`.\n\nOne 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. \n\nOn 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.\n\n## Licence\nSee the LICENCE file.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbasilfx%2Fcourse-face3d","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbasilfx%2Fcourse-face3d","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbasilfx%2Fcourse-face3d/lists"}