This toolchain implements a face anti-spoofing algorithm based on image-quality measures (IQM) and LDA.

The algorithm expects video files as input. A two-class classifier is constructed using a set of IQM. The real accesses are considered as the positive, and the attacks as the negative class.

The toolchain consists of the following steps:

1. Feature extraction. This step extracts IQM features from the frames of the input video.
2. Training. This step trains a binary classifier via LDA, on the training data.
3. Classification. This step classifies the videos in the development-set and the test-set.
4. Evaluation. This step calculates error rates and plots different performance curves.

The toolchain is based upon the work presented in [Chingovska12]. All the algorithms presented there can be readily implemented and used with this toolchain.