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This paper proposes an end-to-end CNN(Convolutional Neural Networks) modelthat uses gram modules with parameters that are approximately 1.2MB in size todetect fake fingerprints. The proposed method assumes that texture is the mostappropriate characteristic in fake fingerprint detection, and implements thegram module to extract textures from the CNN. The proposed CNN structure usesthe fire module as the base model and uses the gram module for textureextraction. Tensors that passed the fire module will be joined with grammodules to create a gram matrix with the same spatial size. After 3 grammatrices extracted from different layers are combined with the channel axis, itbecomes the basis for categorizing fake fingerprints. The experiment resultshad an average detection error of 2.61% from the LivDet 2011, 2013, 2015 data,proving that an end-to-end CNN structure with few parameters that is able to beused in fake fingerprint detection can be designed.

End-to-End Fingerprints Liveness Detection using Convolutional Networks with Gram module