This toolchain implements a parts-based face verification using Intersession Variability Modelling (McCool2013]) described in [McCool2013].

The toolchain is detailed as follows:

• The images from the database are aligned according to the specified eye locations.
• In the preprocessing step the cropped faces are basically filtered. Currently the following algorithms can be used for such purpose: Filters and Tan and Triggs
• In the feature extraction step any local feature algorithm can be used, such as: DCT or LBP.
• The extracted features from the training set are used to train the Universal Background Model (UBM). The algorithm GMM can be used for this purpose.
• For each set of feature vectors of one image, the GMM statistics are extracted (based on the Maximum a Posteriori (MAP) adaption using the UBM as a prior). The algorithm GMM Statistics can be used for this purpose.
• The GMM Statistics of the training set are used to estimate the U subspace. The algorithm ISV can be used for this purpose.
• For each set of GMM Statistics of a given client, the UBM and the U subspace, a client can be enrolled using the algorithm ISV Enroll
• The scoring step for the ISV is defined as the LLR between the client model and the UBM, using the GMM Statistics of a given probe as input. The algorithm ISV Scoring can be used for this purpuse. The main inputs for this algoritm are: the GMM Statistics of a probe, the client model, the UBM, the U subspace.
• The analysis step integrates scores from the development and the test set.