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.