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OpenCV

962
977
+ 1
96
OpenFace

28
98
+ 1
3
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OpenCV vs OpenFace: What are the differences?

OpenCV: Open Source Computer Vision Library. OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform; OpenFace: Free and open source face recognition with deep neural networks. OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google.

OpenCV belongs to "Image Processing and Management" category of the tech stack, while OpenFace can be primarily classified under "Facial Recognition".

Some of the features offered by OpenCV are:

  • C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android
  • More than 47 thousand people of user community and estimated number of downloads exceeding 7 million
  • Usage ranges from interactive art, to mines inspection, stitching maps on the web or through advanced robotics

On the other hand, OpenFace provides the following key features:

  • Detect faces with pre-trained models
  • Transform faces for the neural network
  • Use deep neural networks to reprsent or embed the face on a hypersphere

OpenCV and OpenFace are both open source tools. It seems that OpenCV with 37.1K GitHub stars and 27.4K forks on GitHub has more adoption than OpenFace with 12.5K GitHub stars and 3.06K GitHub forks.

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Pros of OpenCV
Pros of OpenFace
  • 36
    Computer Vision
  • 17
    Open Source
  • 11
    Imaging
  • 9
    Machine Learning
  • 9
    Face Detection
  • 6
    Great community
  • 4
    Realtime Image Processing
  • 2
    Helping almost CV problem
  • 2
    Image Augmentation
  • 3
    Open Source

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- No public GitHub repository available -

What is OpenCV?

OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.

What is OpenFace?

OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google.

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What companies use OpenCV?
What companies use OpenFace?
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What tools integrate with OpenCV?
What tools integrate with OpenFace?
    No integrations found

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    What are some alternatives to OpenCV and OpenFace?
    TensorFlow
    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
    CImg
    It mainly consists in a (big) single header file CImg.h providing a set of C++ classes and functions that can be used in your own sources, to load/save, manage/process and display generic images.
    OpenGL
    It is a cross-language, cross-platform application programming interface for rendering 2D and 3D vector graphics. The API is typically used to interact with a graphics processing unit, to achieve hardware-accelerated rendering.
    PyTorch
    PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
    OpenCL
    It is the open, royalty-free standard for cross-platform, parallel programming of diverse processors found in personal computers, servers, mobile devices and embedded platforms. It greatly improves the speed and responsiveness of a wide spectrum of applications in numerous market categories including gaming and entertainment titles, scientific and medical software, professional creative tools, vision processing, and neural network training and inferencing.
    See all alternatives