davisonyeas / facial_recognition

Facial Recognition System

Facial recognition systems are automated technologies that can identify or verify a person's identity using their facial features. These systems use algorithms and machine learning to analyze and compare facial images with a database of known faces to determine if there is a match.

Acknowledgements

• A Review of Face Recognition Technology
• Design and Implementation of a Real-Time Face Recognition System Based on Artificial Intelligence Techniques

Advantages

This solution provides the following:

• Face recognition accuracy 99.38%
• Ability to identify multiple people at once in real time based on one or more reference images.
• Ability to track and assign a unique identifier to each person in the camera image or video.
• Ability to return a similarity measured between detected faces and reference faces.

Business Case

​ Can be used in access control systems or Online Proctoring systems for student authentication or bank authentication

Technical details

Input 1 (video or image to process, capable of processing): ​​

• mjpeg stream

• rtsp stream

• USB camera devices

• video files (avi, mp4, mkv formats supported)

• standalone image files (.png, .jpg formats supported)

Input 2:

Reference images to which the system compares the faces in the image. (.png, .jpg formats supported)

Outputs:

• Processed video frame

• The faces in the frame (bounding boxes)

For each face:

• Unique Tracking ID (when processing a video file, the same ID on each frame belongs to the same person)

• The most similar reference face ID

• Degree of similarity to the reference face

• Up to 5 facial landmark points

• The system is able to to write the processed video to a video file.

Used By

This project is used by the following:

• Kaduna State University
• Daton

Diagrams

• Schematic block diagram of a Facial Recognition System

• Use case diagram illustrating the system behaviour

• Flow chart of the system

Tech Stack

• Dlib, Numpy, OpenCV, Flask

How to use

• Clone the repo $ git clone https://github.com/Davisonyeas/facial_recognition.git

• Change your directory to the cloned repo and create a Python virtual environment named 'face_recog' $ mkvirtualenv face_recog

• Now, run the following command in your Terminal/Command Prompt to install the libraries required $ pip3 install -r requirements.txt

python app.py

👏 Congratulations

You have completed a Computer Vision project that can now be deployed.

🤝 Contribution

Feel free to file a new issue with a respective title and description on the this face recognition repository. If you already found a solution to your problem, I would love to review your pull request!

❤️ Owner

Made by Davis Onyeoguzoro

Connect

Feel free to mail me for any doubts/query ✉️ [email protected]

Follow me on LinkedIn, https://www.linkedin.com/in/davis-onyeoguzoro/

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