The individuality and persistence of fingerprints is being leveraged for a plethora of day-to-day automated person recognition applications, ranging from social benefits disbursements and unlocking smartphones to law enforcement and border security. While the primary purpose of a fingerprint recognition system is to ensure reliable and accurate user recognition, the security of the system itself can be jeopardized by the use of fingerprint presentation attacks (PAs). A fingerprint PA is defined as a presentation, of a spoof (fake), altered, or cadaver finger, to the data capture system (fingerprint reader) intended to interfere with the recording of the true fingerprint sample/identity, and thereby preventing correct user recognition.In this thesis, we present an automated, accurate, and reliable software-only fingerprint presentation attack detector (PAD), called Fingerprint Spoof Buster. Specifically, we propose a deep convolutional neural network (CNN) based approach utilizing local patches centered and aligned using fingerprint minutiae. The proposed PAD achieves state-of-the-art performance on publicly available liveness detection databases (LivDet) and large-scale government controlled tests as part of the IARPA ODIN program. Additionally, we present a graphical user interface that highlights local regions of the fingerprint image as bonafide or PA for visual examination. This offers significant advantage over existing PAD solutions that rely on a single spoof score for the entire fingerprint image.Deep learning-based solutions are infamously resource intensive (both memory and processing) and require special hardware such as graphical processing units (GPUs). With the goal of real-time inference in low-resource environments, such as smartphones and embedded devices, we propose a series of optimizations including simplifying the network architecture and quantizing model weights (for byte computations instead of floating point arithmetic). These optimizations enabled us to develop a light-weight version of the PAD, called Fingerprint Spoof Buster Lite, as an Android application, which can execute on a commodity smartphone (Samsung Galaxy S8) with a minimal drop in PAD performance (from TDR = 95.7% to 95.3% FDR = 0.2%) in under 100ms.Typically, deep learning-based solutions are considered as "black-box" systems due to the lack of interpretability of their decisions. One of the major limitations of the existing PAD solutions is their poor generalization against PA materials not seen during training. While it is observed that some materials are easier to detect (e.g. EcoFlex) compared to others (e.g. Silgum) when left out from training, the underlying reasons are unknown. We present a framework to understand and interpret the generalization (cross-material) performance of the proposed PAD by investigating the material properties and visualizing the bonafide and PA samples in the multidimensional feature space learned by deep networks. Furthermore, we present two different approaches to improve the generalization performance: (i) a style transfer-based wrapper, called Universal Material Generator (UMG), and (ii) a dynamic approach utilizing temporal analysis of a sequence of fingerprint image frames. The two proposed approaches are shown to significantly improve the generalization performance evaluated on large databases of bonafide and PA samples.Lastly, fingerprint readers based on conventional imaging technologies, such as optical, capacitive, and thermal, only image the 2D surface fingerprint making them an easy target for presentation attacks. In contrast, Optical Coherent Tomography (OCT) imaging technology provides rich depth information, including the internal fingerprint, eccrine (sweat) glands, as well as PA instruments (spoofs) placed over finger skin. As a final contribution, we present an automated PAD approach utilizing cross-sectional OCT depth profile scans which is shown to achieve a TDR of 99.73% FDR of 0.2% on a database of 3,413 bonafide and 357 PA OCT scans, fabricated using 8 different PA materials. We also identify the crucial regions in the OCT scans necessary for PA detection.
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