Related papers: White-Box Evaluation of Fingerprint Recognition Sy…
The NIST Fingerprint Image Quality (NFIQ) algorithm has become a standard method to assess fingerprint image quality. However, in many applications a more accurate and reliable assessment is desirable. In this publication, we report on our…
Over the past two decades, biometric recognition has exploded into a plethora of different applications around the globe. This proliferation can be attributed to the high levels of authentication accuracy and user convenience that biometric…
"The output of a computerised system can only be as accurate as the information entered into it." This rather trivial statement is the basis behind one of the driving concepts in biometric recognition: biometric quality. Quality is nowadays…
The effect of image quality degradation on the verification performance of automatic fingerprint recognition is investigated. We study the performance of two fingerprint matchers based on minutiae and ridge information under varying…
While deep learning models have greatly improved the performance of most artificial intelligence tasks, they are often criticized to be untrustworthy due to the black-box problem. Consequently, many works have been proposed to study the…
Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. Experimental studies…
Machine learning models are becoming increasingly popular in different types of settings. This is mainly caused by their ability to achieve a level of predictive performance that is hard to match by human experts in this new era of big…
One of the most challenging problems in fingerprint recognition continues to be establishing the identity of a suspect associated with partial and smudgy fingerprints left at a crime scene (i.e., latent prints or fingermarks). Despite the…
For the highly imbalanced credit card fraud detection problem, most existing methods either use data augmentation methods or conventional machine learning models, while neural network-based anomaly detection approaches are lacking.…
The study identifies a clear evolution from traditional methods to more advanced machine learning approaches. Current algorithms face persistent challenges, including degraded image quality, damaged ridge structures, and background noise,…
Presentation Attack Detection (PAD) systems are usually designed independently of the fingerprint verification system. While this can be acceptable for use cases where specific user templates are not predetermined, it represents a missed…
The security of messaging applications against person-in-the-middle attacks relies on the authenticity of the exchanged keys. For users unable to meet in person, a manual key fingerprint verification is necessary to ascertain key…
Background: Explainability in phishing detection model can support a further solution of phishing attack mitigation by increasing trust and understanding how phishing can be detected. Objective: The aims of this study to determine and best…
Fingerprint evidence plays an important role in a criminal investigation for the identification of individuals. Although various techniques have been proposed for fingerprint classification and feature extraction, automated fingerprint…
Data-trained predictive models see widespread use, but for the most part they are used as black boxes which output a prediction or score. It is therefore hard to acquire a deeper understanding of model behavior, and in particular how…
Practical video analytics systems that are deployed in bandwidth constrained environments like autonomous vehicles perform computer vision tasks such as face detection and recognition. In an end-to-end face analytics system, inputs are…
Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most…
Device fingerprinting leverages attributes that capture heterogeneity in hardware and software configurations to extract unique and stable fingerprints. Fingerprinting countermeasures attempt to either present a uniform fingerprint across…
Contactless fingerprint recognition enables hygienic and convenient biometric authentication but poses new challenges for spoof detection due to the absence of physical contact and traditional liveness cues. Most existing methods rely on…
Till now, few work has been done to analyze the performances of joint fingerprint embedding and decryption schemes. In this paper, the security of the joint fingerprint embedding and decryption scheme proposed by Kundur et al. is analyzed…