Fingerprint Disentanglement for Presentation Attack Generalization Across Sensors and Materials

Abstract

In today’s digital era, biometric authentication has become increasingly widespread for verifying a user across a range of applications, from unlocking a smartphone to securing high-end systems. Various biometric modalities such as fingerprint, face, and iris offer a distinct way to recognize a person automatically. Fingerprints are one of the most prevalent biometric modalities. They are widely utilized in security systems owing to their remarkable reliability, distinctiveness, invariance over time and user convenience. Nowadays, automatic fingerprint recognition systems have become a prime target for attackers. Attackers fabricate fingerprints using materials like playdoh and gelatin, making it hard to distinguish them from live fingerprints. This way of circumventing biometric systems is called a presentation attack (PA). To identify such attacks, a PA detector is added to these systems. Deep learning-based PA detectors require large amounts of data to distinguish PA fingerprints from live ones. However, there exists significantly less training data with novel sensors and materials. Due to this, PA detectors do not generalize well on introducing unknown sensors or materials. It is incredibly challenging to physically fabricate an extensive train dataset of high-quality counterfeit fingerprints generated with novel materials captured across multiple sensors. Existing fingerprint presentation attack detection (FPAD) solutions improve cross-sensor and cross-material generalization by utilizing styletransfer-based augmentation wrappers over a two-class PA classifier. These solutions generate large artificial datasets for training by using style transfer which learns the style properties from a few samples obtained from the attacker. They synthesize data by learning the style as a single entity, containing both sensor and material characteristics. However, these strategies necessitate learning the entire style upon adding a new sensor for an already known material or vice versa. This thesis proposes a decomposition-based approach to improve cross-sensor and cross-material FPAD generalization. We model presentation attacks as a combination of two underlying components, i.e., material and sensor, rather than the entire style. By utilizing this approach, our method can generate synthetic patches upon introducing either a new sensor, a new material, or both. We perform two different methods of fingerprint factorization - traditional and deep-learning based. Traditional factorization of fingerprints into sensor and material representations using tensor decomposition establishes a baseline using machine learning for our hypothesis. The deep-learning method uses a decompositionbased augmentation wrapper for disentangling fingerprint style. The wrapper improves cross-sensor and cross-material FPAD, utilizing one fingerprint image of the target sensor and material. We also reduce vi vii computational complexity by generating compact representations and utilizing lesser combinations of sensors and materials to produce several styles. Our approach enables us to generate a large variety of samples using a limited amount of data, which helps improve generalization