88 research outputs found
TypeFormer: Transformers for Mobile Keystroke Biometrics
The broad usage of mobile devices nowadays, the sensitiveness of the
information contained in them, and the shortcomings of current mobile user
authentication methods are calling for novel, secure, and unobtrusive solutions
to verify the users' identity. In this article, we propose TypeFormer, a novel
Transformer architecture to model free-text keystroke dynamics performed on
mobile devices for the purpose of user authentication. The proposed model
consists in Temporal and Channel Modules enclosing two Long Short-Term Memory
(LSTM) recurrent layers, Gaussian Range Encoding (GRE), a multi-head
Self-Attention mechanism, and a Block-Recurrent structure. Experimenting on one
of the largest public databases to date, the Aalto mobile keystroke database,
TypeFormer outperforms current state-of-the-art systems achieving Equal Error
Rate (EER) values of 3.25% using only 5 enrolment sessions of 50 keystrokes
each. In such way, we contribute to reducing the traditional performance gap of
the challenging mobile free-text scenario with respect to its desktop and
fixed-text counterparts. Additionally, we analyse the behaviour of the model
with different experimental configurations such as the length of the keystroke
sequences and the amount of enrolment sessions, showing margin for improvement
with more enrolment data. Finally, a cross-database evaluation is carried out,
demonstrating the robustness of the features extracted by TypeFormer in
comparison with existing approaches
Privacy-Preserving Biometric Authentication
Biometric-based authentication provides a highly accurate means of authentication without requiring the user to memorize or possess anything. However, there are three disadvantages to the use of biometrics in authentication; any compromise is permanent as it is impossible to revoke biometrics; there are significant privacy concerns with the loss of biometric data; and humans possess only a limited number of biometrics, which limits how many services can use or reuse the same form of authentication.
As such, enhancing biometric template security is of significant research interest. One of the methodologies is called cancellable biometric template which applies an irreversible transformation on the features of the biometric sample and performs the matching in the transformed domain. Yet, this is itself susceptible to specific classes of attacks, including hill-climb, pre-image, and attacks via records multiplicity.
This work has several outcomes and contributions to the knowledge of privacy-preserving biometric authentication. The first of these is a taxonomy structuring the current state-of-the-art and provisions for future research. The next of these is a multi-filter framework for developing a robust and secure cancellable biometric template, designed specifically for fingerprint biometrics. This framework is comprised of two modules, each of which is a separate cancellable fingerprint template that has its own matching and measures. The matching for this is based on multiple thresholds. Importantly, these methods show strong resistance to the above-mentioned attacks. Another of these outcomes is a method that achieves a stable performance and can be used to be embedded into a Zero-Knowledge-Proof protocol. In this novel method, a new strategy was proposed to improve the recognition error rates which is privacy-preserving in the untrusted environment. The results show promising performance when evaluated on current datasets
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
Advanced Biometrics with Deep Learning
Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others
An exploration of dynamic biometric performance using device interaction and wearable technologies
With the growth of mobile technologies and internet transactions, privacy issues
and identity check became a hot topic in the past decades. Mobile biometrics
provided a new level of security in addition to passwords and PIN, with a multitude of modalities to authenticate subjects. This thesis explores the verification
performance of behavioural biometric modalities, as previous studies in literature
proved them to be effective in identifying individual behaviours and guarantee
robust continuous authentication. In addition, it addresses open issues such as
single sample authentication, quality measurements for behavioural data, and fast
electrocardiogram capture and biometric verification. The scope of this project
is to assess the performance and stability of authentication models for mobile
and wearable devices, with ceremony based tasks and a framework that includes
behavioural and electrocardiogram biometrics.
The results from the experiments suggest that a fast verification, appliable on real
life scenarios (e.g. login or transaction request), with a single sample request and
the considered modalities (Swipe gestures, PIN dynamics and electrocardiogram
recording) can be performed with a stable performance. In addition, the novel
fusion method implemented greatly reduced the authentication error.
As additional contribution, this thesis introduces to a novel pre-processing algorithm for faulty Swipe data removal. Lastly, a theoretical framework comprised of
three different modalities is proposed, based on the results of the various experiments conducted in this study. It's reasonable to state that the findings presented
in this thesis will contribute to the enhancement of identity verification on mobile
and wearable technologies
Biometric information analyses using computer vision techniques.
Biometric information analysis is derived from the analysis of a series of physical and biological characteristics of a person. It is widely regarded as the most fundamental task in the realms of computer vision and machine learning. With the overwhelming power of computer vision techniques, biometric information analysis have received increasing attention in the past decades. Biometric information can be analyzed from many sources including iris, retina, voice, fingerprint, facial image or even the way one walks with. Facial image and gait, because of their easy availability, are two preferable sources of biometric information analysis.
In this thesis, we investigated the development of most recent computer vision techniques and proposed various state-of-the-art models to solve the four principle problems in biometric information analysis including the age estimation, age progression, face retrieval and gait recognition.
For age estimation, the modeling has always been a challenge. Existing works model the age estimation problem as either a classification or a regression problem. However, these two types of models are not able to reveal the intrinsic nature of human age. To this end, we proposed a novel hierarchical framework and a ordinal metric learning based method. In the hierarchical framework, a random forest based clustering method is introduced to find an optimal age grouping protocol. In the ordinal metric learning approach, the age estimation is solved by learning an subspace where the ordinal structure of the data is preserved. Both of them have achieved state-of-the-art performance.
For face retrieval, specifically under a cross-age setting, we first proposed a novel task, that is given two images, finding the target image which is supposed to have the same identity with the first input and the same age with the second input. To tackle this task, we proposed a joint manifold learning method that can disentangle the identity with the age information. Accompanied with two independent similarity measurements, the retrieval can be easily performed.
For aging progression, we also proposed a novel task that has never been considered. We devoted to fuse the identity of one image with the age of another image. By proposing a novel framework based on generative adversarial networks, our model is able to generate close-to-realistic images.
Lastly, although gait recognition is an ideal long-distance biometric information task that makes up the shortfall of facial image, existing works are not able to handle large scale data with various view angles. We proposed a generative model to solve this term and achieved promising results. Moreover, our model is able to generate evidences for forensic usage
Biometric Systems
Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study
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