Executing Baseline Algorithms

In this section we introduce the baselines available in this pakcage. To execute one of then in the databases available just run the following command:

$ bob bio pipelines vanilla-biometrics [DATABASE_NAME] [BASELINE]

Note

Both, [DATABASE_NAME] and [BASELINE] can be either python resources or python files.

Please, refer to bob.bio.base for more information.

Baselines available

The algorithms below constains all the face recognition baselines available. It is split in two groups, before and after deep learning era.

Before Deep learning era

  • eigenface: The eigenface algorithm as proposed by [TP91]. It uses the pixels as raw data, and applies a Principal Component Analysis (PCA) on it.

  • lda: The LDA algorithm applies a Linear Discriminant Analysis (LDA), here we use the combined PCA+LDA approach [ZKC98]

  • gabor_graph: This method extract grid graphs of Gabor jets from the images, and computes a Gabor phase based similarity [GHW12].

  • lgbphs: Local Gabor binary pattern histogram sequence (LGBPHS) implemented in [ZSG05]

Deep learning baselines

  • facenet-sanderberg: FaceNet trained by David Sanderberg

  • inception-resnetv2-msceleb: Inception Resnet v2 model trained using the MSCeleb dataset in the context of the work published by [TFP18]

  • inception-resnetv1-msceleb: Inception Resnet v1 model trained using the MSCeleb dataset in the context of the work published by [TFP18]

  • inception-resnetv2-casiawebface: Inception Resnet v2 model trained using the Casia Web dataset in the context of the work published by [TFP18]

  • inception-resnetv1-casiawebface: Inception Resnet v1 model trained using the Casia Web dataset in the context of the work published by [TFP18]

  • arcface-insightface: Arcface model from Insightface