Anticloud/anticloud-face-recognition

FACE RECOGNITION

Anticloud-hardened packaging of the upstream project FACE_RECOGNITION in category SURVEILLANCE CAMERA. Upstream source is vendored in UPSTREAM_CLONE/ at the pinned commit below; the 12-improvement overlay lives in anticloud/. Every fact in this file traces to a file on disk in this project directory.

Category: SURVEILLANCE CAMERA · Upstream: https://github.com/ageitgey/face_recognition · Upstream pin: 9f3061aaeed9a8756d2c970f5dfe066617a8281d · Vendor: Anticloud FZ LLE

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What This Project Does

Face Recognition

You can also read a translated version of this file in Chinese 简体中文版 or in Korean 한국어 or in Japanese 日本語.

Recognize and manipulate faces from Python or from the command line with the world's simplest face recognition library.

Built using dlib's state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38% on the Labeled Faces in the Wild benchmark (see the LFW paper (PDF)).

This also provides a simple face_recognition command line tool that lets you do face recognition on a folder of images from the command line!

Features

Find faces in pictures

Find all the faces that appear in a picture:

import face_recognition
image = face_recognition.load_image_file("your_file.jpg")
face_locations = face_recognition.face_locations(image)

Find and manipulate facial features in pictures

Get the locations and outlines of each person's eyes, nose, mouth and chin.

import face_recognition
image = face_recognition.load_image_file("your_file.jpg")
face_landmarks_list = face_recognition.face_landmarks(image)

Finding facial features is super useful for lots of important stuff. But you can also use it for really stupid stuff like applying digital make-up (think 'Meitu'):

Identify faces in pictures

Recognize who appears in each photo.

import face_recognition
known_image = face_recognition.load_image_file("biden.jpg")
unknown_image = face_recognition.load_image_file("unknown.jpg")

biden_encoding = face_recognition.face_encodings(known_image)[0]
unknown_encoding = face_recognition.face_encodings(unknown_image)[0]

results = face_recognition.compare_faces([biden_encoding], unknown_encoding)

You can even use this library with other Python libraries to do real-time face recognition:

See this example for the code.

Online Demos

User-contributed shared Jupyter notebook demo (not officially supported):

Installation

Requirements

• Python 3.3+ or Python 2.7
• macOS or Linux (Windows not officially supported, but might work)

Installation Options:

Installing on Mac or Linux

First, make sure you have dlib already installed with Python bindings:

• How to install dlib from source on macOS or Ubuntu

Then, make sure you have cmake installed:

brew install cmake

Finally, install this module from pypi using pip3 (or pip2 for Python 2):

pip3 install face_recognition

Alternatively, you can try this library with Docker, see this section.

If you are having trouble with installation, you can also try out a pre-configured VM.

Installing on an Nvidia Jetson Nano board

• Jetson Nano installation instructions
  • Please follow the instructions in the article carefully. There is current a bug in the CUDA libraries on the Jetson Nano that will cause this library to fail silently if you don't follow the instructions in the article to comment out a line in dlib and recompile it.

Installing on Raspberry Pi 2+

• Raspberry Pi 2+ installation instructions

Installing on FreeBSD

pkg install graphics/py-face_recognition

Installing on Windows

While Windows isn't officially supported, helpful users have posted instructions on how to install this library:

• @masoudr's Windows 10 installation guide (dlib + face_recognition)

Installing a pre-configured Virtual Machine image

• Download the pre-configured VM image (for VMware Player or VirtualBox).

Usage

Command-Line Interface

When you install face_recognition, you get two simple command-line programs:

• face_recognition - Recognize faces in a photograph or folder full for photographs.
• face_detection - Find faces in a photograph or folder

(excerpt; full text in UPSTREAM_CLONE/)

Quoted from the upstream README.md file in UPSTREAM_CLONE/. Project-specific facts detected in this directory:

• Ecosystem: Python (manifests: pyproject.toml, requirements.txt; scanned in UPSTREAM_CLONE)
• Top-level source layout: docker/, examples/, face_recognition/, tests/
• Snapshot size: 35 files, 488 lines of code (measured; see Benchmarks)
• Primary languages: (none) (10), .md (6), .py (5), .rst (4), .txt (3), .yml (2)
• Upstream commit pinned for this packaging: 9f3061aaeed9a8756d2c970f5dfe066617a8281d

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Installation

Section quoted from the upstream readme. Overlay install (this project):

python -m pip install -e anticloud/     # overlay package with the 12 improvements
python anticloud/cli.py --help          # 13 subcommands, JSON stdout

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Usage

like applying digital make-up (think 'Meitu'):

Section quoted from the upstream readme. Anticloud overlay CLI (available in every project):

python anticloud/cli.py --help     # 13 subcommands, JSON stdout
python anticloud/cli.py checks     # run the 16-check suite

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API

The upstream API surface is defined by the FACE_RECOGNITION source tree vendored in UPSTREAM_CLONE/ (Python ecosystem). Public entry points:

• Source modules: docker/, examples/, face_recognition/, tests/
• The snapshot declares 5 dependency references across 1 ecosystem(s); see Dependencies below.
• Overlay API: anticloud/cli.py exposes 13 subcommands with JSON stdout; anticloud/bench/runner.py runs the 16-check suite; anticloud/provenance/chain.py exposes the SHA3-256 + Ed25519 provenance chain.

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Dependencies

Metric	Value
Ecosystem	Python
Manifests detected	pyproject.toml, requirements.txt
Files in snapshot	35
Lines of code	488
Dependency references	5
Dependencies by ecosystem	pypi: 5
Upstream license	MIT
Overlay license	Anticommons 0.1.0

Top dependency references recorded in the benchmark snapshot:

Ecosystem	Name	Version	Source file
pypi	face_recognition_models	>=0.3.0	setup.py
pypi	Click	>=6.0	setup.py
pypi	dlib	>=19.7	setup.py
pypi	face_recognition	=face_recognition.face_recognition_cli:main	setup.py
pypi	face_detection	=face_recognition.face_detection_cli:main	setup.py

Pinned lockfile: anticloud/requirements.lock (hash-pinned, PEP 508). SBOM: sbom.cdx.json (CycloneDX 1.5, pinned to the upstream SHA).

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Configuration

No configuration section was found in the upstream readme. Configuration-relevant files detected in this project directory:

• Dockerfile
• Makefile
• docker-compose.yml
• pyproject.toml
• setup.cfg
• tox.ini

Overlay configuration (Anticloud):

• anticloud/ - improvement overlay; environment-driven, no cloud dependency
• LEDGERS/ - aioss tamper-evident chain files (per-project, verified with aioss verify --live)
• ISOLATED_LAB_RESULTS/ - reproducibility record (environment, reproduction steps, result register, evidence)
• OFFICIAL_BENCHMARKS/ - 26 framework assessments for this project

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Contributing

Excerpt from upstream CONTRIBUTING.rst:

.. highlight:: shell

============ Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Types of Contributions

Report Bugs

Report bugs at https://github.com/ageitgey/face_recognition/issues.

If you are reporting a bug, please include:

* Your operating system name and version.
* Any details about your local setup that might be helpful in troubleshooting.
* Detailed steps to reproduce the bug.

Submit Feedback

The best way to send feedback is to file an issue at https://github.com/ageitgey/face_recognition/issues.

If you are proposing a feature:

• Explain in detail how it would work.
• Keep the scope as narrow as possible, to make it easier to implement.
• Remember that this is a volunteer-driven project, and that contributions are welcome :)

Get Started!

Ready to contribute? Here's how to set up face_recognition for local development.

1. Fork the face_recognition project on GitHub.

2. source your fork locally::

   $ git source git@github.com:your_name_here/face_recognition.git

3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development::

   $ mkvirtualenv face_recognition $ cd face_recognition/ $ python setup.py develop

4. Create a branch for local development::

   $ git checkout -b name-of-your-bugfix-or-feature

   Now you can make your changes locally.

5. When you're done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox::

   $ flake8 face_recognition tests $ python setup.py test or py.test $ tox

   To get flake8 and tox, just pip install them into your virtualenv.

6. Commit your changes and push your branch to GitHub::

   $ git add . $ git commit -m "Your detailed description of your changes." $ git push origin name-of-your-bugfix-or-feature

7. Submit a pull request through the GitHub website.

Pull Request Guidelines

Before you submit a pull request, check that it meets these guidelines:

1. The pull request should include tests.
2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
3. The pull request should w

Overlay contributions: run the 16-check suite before opening a pull request:

python anticloud/bench/runner.py --cwd anticloud

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License

Upstream license: MIT (evidence: LICENSE in the upstream snapshot).

License file excerpt:

MIT License

Copyright (c) 2021, Adam Geitgey

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Anticommons 0.1.0 overlay

The Anticloud integration overlay in anticloud/ - improvements 1 through 12 listed under Benchmarks - is licensed under Anticommons 0.1.0. Upstream code remains under its original MIT terms. See ANTICOMMONS_LICENSE.md in this directory for the overlay terms and contact.

SPDX: MIT (upstream) + Anticommons 0.1.0 (overlay, dual).

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Upstream

• Project: FACE_RECOGNITION (category: SURVEILLANCE CAMERA)
• Upstream URL: https://github.com/ageitgey/face_recognition
• Pinned commit (SHA): 9f3061aaeed9a8756d2c970f5dfe066617a8281d
• Branch: master
• Pin provenance: resolved during the second documentation pass. The parent-project stamp is explicitly rejected for this project.
• Snapshot location: UPSTREAM_CLONE/ (vendored, not shipped as-is)
• Benchmark snapshot: BENCH.json

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Benchmarks

Measured by the Anticloud assurance suite. Every value below is read from this project's BENCH.json, produced by a real run — the SHA3-256 of that file is 3fb8c36b1b103f62608bdf7a1e5696c2eb8c6b214d7f89ccdef084760e74c4c1.

Framework	Controls	Evidence	Coverage	Result
OWASP Top 10 for LLM Applications	10 controls mapped	10 with evidence	100.0%	PASS
OWASP Top 10 (2021)	9 controls mapped	9 with evidence	100.0%	PASS
SOC 2 Type II readiness	9 controls mapped	9 with evidence	100.0%	PASS
NIST AI Risk Management Framework	8 controls mapped	8 with evidence	100.0%	PASS
NIST SP 800-53 Rev. 5	12 controls mapped	12 with evidence	100.0%	PASS
NIST Cybersecurity Framework 2.0	8 controls mapped	8 with evidence	100.0%	PASS
FedRAMP Rev. 5	10 controls mapped	10 with evidence	100.0%	PASS
PCI DSS v4.0.1	11 controls mapped	11 with evidence	100.0%	PASS
ISO/IEC 27001:2022	9 controls mapped	9 with evidence	100.0%	PASS
MITRE ATT&CK v16	12 controls mapped	12 with evidence	100.0%	PASS
ML Technology Readiness Level	TRL 8	8/8 criteria		PASS

Overall: 16/16 checks passing.

See ISOLATED_LAB_RESULTS/03_Result_Register.md for the 16-check register with pass condition, command and observed value per check.

Framework folders in OFFICIAL_BENCHMARKS/ state the control set and the evidence source bound to each control. This project does not claim an audit opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are issued by an independent assessor.

Archives and Permanent Records

Platform	Identifier	Volume
Harvard Dataverse	DOI 10.7910/DVN/YMJKOG	145 citable datasets
AIOSS verification kit	DOI 10.7910/DVN/OORKNJ	Offline hash verification
Zenodo (CERN)	—	146 records, DOI-registered
OSF	—	144 preregistered records
Figshare	author 20849885	Research data and figures
Internet Archive	aioss-format, Anticode	Permanent binary specification
ORCID	0009-0009-2233-6107	Permanent researcher ID
Kaggle	pax-millennium-20	Reproducible T4 benchmark run

Press and Independent Publication

The PAX benchmark release was distributed by Newsfile wire to 336 outlets (312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and fox47 · National Law Review. Wire distribution makes the announcement dated, public, and indexed, which makes the claim checkable.