Technical requirements
The following technologies and installations are required to build the code in this chapter:
- OpenCV v4 (compiled with the
face contribmodule) - Boost v1.66+
Build instructions for the preceding components listed, as well as the code to implement the concepts presented in this chapter, will be provided in the accompanying code repository.
To run the facemark detector, a pre-trained model is required. Although training the detector model is certainly possible with the APIs provided in OpenCV, some pre-trained models are offered for download. One such model can be obtained from https://raw.githubusercontent.com/kurnianggoro/GSOC2017/master/data/lbfmodel.yaml, supplied by the contributor of the algorithm implementation to OpenCV (during the 2017 Google Summer of Code (GSoC)).
The facemark detector can work with any image; however, we can use a prescribed dataset of facial photos and videos that are used to benchmark facemark algorithms. Such a dataset is 300-VW, available through...
