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Lab (PS) Topic Description

  • A biometric presentation attack detection (PAD) system is typically a binary classifier, discriminating a sample image taken by the sensor from a human body part (real sample) from a sample image taken by the sensor from a presentation attack instrument (PAI), an artefact used to deceive the system by pretending to be a real / genuine human body part (fake sample). Thus we have real samples, and fake samples which are used to train the binary PAD classifier.
  • Fake samples are difficult to produce, as for each specimen a PAI needs to be created and scanned by the sensor. This is why people have come up with synthetic PAI (fake) samples which are much easier to create. We will work with different types of such synthetic PAI samples and will investigate if these can be used instead of / in addition to fake samples. To summarise, we have three different types of data: (1) Real samples (2) Fake samples and (3) Synthetic samples
  • The major task in this lab is to investigate if type (3) samples can replace type (2) samples as these are much easier to create.
  • First we establish the groundtruth: the PAD system is trained with type (1) and (2) samples, and also evaluated with those (in a five-fold cross validation protocol).
  • Subsequently, we train the PAD sytem with type (1) vs. (3) samples, and evaluate it with type (1) vs. (2) samples (we want to see if training with synthetic data can lead to reliable results to discriminate real from fake samples).
  • Finally, we train again like for groundtruth results, but sucessively reduce the number of fake samples in the training set. Missing ones are replaced by synthetic ones (thus in training we train type (1) vs. a mixture of type (2) and (3), evaluation is done - as always - with type (1) vs. type (2) samples).
  • We use the k-nearest neighbour classifier as PAD binary classifier, different groups use different feature extractions schemes as input to the classifier.