Fingerprint Presentation Attack Detection in Open-Set Scenario Using Transient Liveness Factor

Abstract

Background: In recent history, fingerprint presentation attack detection (FPAD) proposal came out in a variety of ways. A close-set approach uses pattern classification technique that best suits to a specific context and goal. Openset approach works fine in wider context, which is relatively robust with new fabrication material and independent of sensor type. In both case results were promising but not too generalizable because of unseen condition not fitting into method used. It is clear, the two key challenges in FPAD system, sensor interoperability and robustness with new fabrication materials not addressed to date. Objective: To address above challenge a liveness detection model is proposed using live sample using transient liveness factor and one-class CNN. Methods: In our architecture, liveness is predicted by using the fusion rule, score level fusion of two decisions. Here, ‘n’ high quality live samples are initially trained for quality. We have observed that fingerprint liveness information is ‘transitory’ in nature, a variation in the different live sample is natural. Thus, each live sample has a ‘transient liveness’ (TL) information. We use no-reference (NR) image quality measure (IQM) as a transient value corresponding to each live sample. A consensus agreement is collectively reached in transient value to predict adversarial input. Further, live sample at server are trained with augmented inputs on the one-class classifier to predict the outlier. So, by using the fusion rule, score level fusion of consensus agreement and appropriately characterized negative cases (or outliers) predicts liveness. Results: Our approach uses high quality 30-live sample only, out of 90 images available in dataset to reduce learning time. We used Time Series images from LivDet competition 2015. It has 90-live images and 45-spoof images made from Bodydouble, Ecoflex and Playdoh of each person. Fusion rule results in 100% accuracy in recognising live as live. Conclusion: We have presented an architecture for liveness-server for extraction/updating transient liveness factor. Our work explained here a significant step forward towards generalized and reproducible process with a consideration towards the provision for the universal scheme as a need of today. The proposed TLF approach has a solid presumption; it will address dataset heterogeneity as it incorporates wider scope-context. Similar results with other dataset are under validation. Implementation seems difficult now but have several advantages when carried out during the transformative process.