FaceAB

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Introduction

The FaceAB software is a graphical interface program that uses advanced machine learning algorithms to perform real-time face recognition, age prediction, and gender prediction. Developed by Mohamad Aboud, the software is written in Python and utilizes several libraries including :

• opencv.
• cmake.
• dlib.
• face_recognition.
• scikit-learn.
• mediapipe.
• flet.

The FaceAB program offers a user-friendly interface that allows users to easily set up and configure the face recognition system to their specific needs. The program uses a deep learning-based approach to face recognition, which allows it to accurately identify individuals even in challenging lighting conditions and when faces are partially occluded.

In addition to its real-time face recognition capabilities, the FaceAB program also offers offline batch processing capabilities. This allows users to process large amounts of video or image data and generate reports on the individuals that are present in the data.

Overall, the FaceAB program is a versatile and powerful tool for implementing real-time face recognition in a variety of applications. Its user-friendly interface and advanced machine learning algorithms make it a valuable asset for any organization looking to incorporate face recognition technology into their systems.

Technical details

The FaceAB software uses several libraries and technologies to perform real-time face recognition, age prediction, and gender prediction. These include:

1. opencv: This library is used to perform a variety of computer vision tasks, including face detection, image preprocessing, and feature extraction.

2. cmake: This library is used to build system for C and C++ projects that is used to generate platform-specific build files for a variety of build tools.

3. dlib: This library is used to for machine learning and computer vision tasks. It provides a wide range of algorithms and tools for tasks such as facial recognition, object detection, and image segmentation.

4. face_recognition: This library is used for the core face recognition functionality of the FaceAB software. It uses a deep learning-based approach to face recognition, allowing it to accurately identify individuals even in challenging lighting conditions and when faces are partially occluded.

5. scikit-learn: is a popular library for machine learning in Python. It provides a wide range of algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.

6. mediapipe: This library is used to process video streams in real-time, allowing the FaceAB software to recognize faces in live video feeds.

7. flet: This library is a framework that allows building interactive multi-user web, desktop and mobile applications in flutter language.

In addition to these libraries, the FaceAB software also utilizes advanced machine learning algorithms, such as K-nearest neighbor (kNN), to accurately recognize faces .

Installation and usage

To install and use the FaceAB software, you will need to have the following libraries and technologies installed on your system:

Prerequisites

• Python: This is the programming language in which the FaceAB software is written. You can download and install Python from here.

> python --version
Python 3.9.1

Requisites

• opencv: This library is used to perform a variety of computer vision tasks. You can install it using the following command:

> pip install opencv-python==4.6.0.66

• cmake: To install and use CMake in Python, you will need to have CMake, a C++ compiler, and the Python development headers and libraries installe. You can install it using the following command:

> pip install cmake==3.25.0

• dlib: Because dlib is developed in a C-based programming language, it needs CMake. You can install it using the following command:

*note: If you encounter a problem during the installation, you can see this website

> pip install dlib==19.24.0

• face_recognition: This library is used for the core face recognition functionality of the FaceAB software. You can install it using the following command:

> pip install face-recognition==1.3.0

• scikit-learn: This library is used to train the knn model used for fast facial recognition. You can install it using the following command:

> pip install scikit-learn==1.2.0

• mediapipe: This library is used to recognize body and vertex points in real time. You can install it using the following command:

> pip install mediapipe==0.9.0.1

• flet: This library is used to build the graphical interface. You can install it using the following command:

> pip install flet==0.2.4

Or you can download all libraries via the following command:

> pip install -r requirements.txt

Once you have installed these libraries, you can download the FaceAB software from https://github.com/MohamadAboud/FaceAB. To run the software, open a terminal or command prompt and navigate to the directory where you downloaded the software. Then, use the following command to run the program:

> python main.py

The FaceAB software will then launch and you can use the user-friendly interface to set up and configure the face recognition system to your specific needs. You can then use the software to recognize faces in real-time video streams or process offline batches of video or image data.

Overall, the FaceAB software is easy to install and use.

Limitations and future work

The FaceAB software is a powerful and versatile tool for implementing real-time face recognition, but it does have some limitations. Some of the current limitations of the software include:

The software is currently only available for use with Python. This may limit its compatibility with other programming languages and systems.

The software relies on a deep learning-based approach to face recognition, which can be computationally intensive. This may limit its performance on systems with limited resources.

The software currently only offers real-time face recognition and offline batch processing capabilities.

To address these limitations and improve the capabilities of the FaceAB software, there are several areas of future work that could be pursued. Some potential directions for future work include:

Developing versions of the FaceAB software for use with other programming languages, such as C++ or Java. This would expand the compatibility of the software and allow it to be used in a wider range of applications and systems.

Investigating ways to optimize the performance of the software, such as through the use of specialized hardware or more efficient algorithms. This would allow the software to run more efficiently and effectively on a wider range of systems.

Conclusion

FaceAB is a graphical interface software for real-time face recognition, age prediction, and gender prediction. Developed by Mohamad Aboud, the software uses advanced machine learning algorithms and technologies, such as opencv, cmake, dlib, face_recognition, scikit-learn, mediapipe, cvzone, and flet, to accurately identify individuals in real-time video streams. The software offers a user-friendly interface and offline batch processing capabilities, making it a versatile and powerful tool. While the software has some limitations, there are opportunities for future work to expand its capabilities and improve its performance.

References

To learn more about the FaceAB software and its capabilities, please see the following references:

• Mohamad Aboud's profile: https://github.com/MohamadAboud

• OpenCV:

  • OpenCV documentation: https://docs.opencv.org/4.4.0/
  • OpenCV tutorial: https://docs.opencv.org/4.4.0/d9/df8/tutorial_root.html
  • OpenCV GitHub repository: https://github.com/opencv/opencv

• CMake:

  • CMake documentation: https://cmake.org/documentation/
  • CMake tutorial: https://cmake.org/cmake/help/latest/guide/tutorial/index.html
  • CMake GitHub repository: https://github.com/Kitware/CMake

• Dlib:

  • dlib documentation: http://dlib.net/python/index.html
  • dlib GitHub repository: https://github.com/davisking/dlib

• Face_recognition:

  • face_recognition documentation: https://face-recognition.readthedocs.io/en/latest/
  • face_recognition GitHub repository: https://github.com/ageitgey/face_recognition

• Scikit-learn:

  • scikit-learn documentation: https://scikit-learn.org/stable/documentation.html
  • scikit-learn tutorial: https://scikit-learn.org/stable/tutorial/index.html
  • scikit-learn GitHub repository: https://github.com/scikit-learn/scikit-learn

• MediaPipe:

  • MediaPipe documentation: https://google.github.io/mediapipe/
  • MediaPipe GitHub repository: https://github.com/google/mediapipe

• Flet:

  • Flet documentation: https://flet.dev/docs/
  • Flet tutorial: https://flet.dev/docs/tutorials
  • Flet GitHub repository: https://github.com/flet-dev/flet

These references provide more detailed information about the FaceAB software, its capabilities, and its underlying technologies.