Color Adaptive Quantized Patterns for Presentation Attack Detection in Ocular Biometric Systems
Kiran Bylappa Raja, Raghavendra Ramachandra, Christoph Busch · 2016
The challenges of presentation attacks (spoofing attacks) at sensor level is increasing for biometric systems due to the evolving method of artefact presentation. The sophisticated attacks now employ high quality printed artefacts and electronic screens to present the biometric samples which make it difficult to separate the real presentations and artefact presentations. In this work, we propose a new scheme to detect the artefacts in both NIR and visible spectrum biometric sensors for ocular biometric systems using a new set of feature descriptor. The scheme employs adaptive and quantized texture patters obtained from local microfeatures and global spatial features for different color channels in an image. Further, the texture descriptors are used to learn a spectrally regressed discriminant classifier to classify the normal ocular images against the artefact ocular images. The proposed scheme is used to perform extensive experiments on 5 publicly available ocular datasets including 2 datasets acquired in NIR domain and two datasets acquired using smartphones along with a dataset acquired using high quality camera. The experiments conducted on all the datasets have consistently indicated the robust performance against attacks by showing a classification error of 0%.