EResNeXt: A Multiple Representation and Attention Based Technique for Fingerprint Presentation Attack Detection in Unknown Presentation Attack Instruments Scenario

Fingerprint biometrics are one of the most common authentication mechanisms. However, such systems are often compromised by presentation attacks by presentation attack instruments. Most of the fingerprint presentation attack detection approaches show poor performance due to the large variation in presentation attack instruments and limited feature representation of input fingerprint. Therefore this article proposes a hybrid model of shallow and deep features with multiple representations of input fingerprints. To obtain these shallow and deep features first we have enhanced the texture of the input fingerprint through a novel median adaptive local binary pattern filter and existing binarised statistical image feature. After that, the input fingerprint image and two textured enhanced images are concatenated along with the channel dimension for multiple representations. Finally, an extended ResNeXt architecture with channel and spatial attention (EResNeXt) has been used for relevant feature extraction and presentation attack detection. The proposed model (EResNeXt) has been assessed on LivDet-2015 and Livdet-2017 datasets and provides significant results in unknown presentation attack instrument scenarios.