Biometric Signature Verification Using Recurrent Neural Networks

“© 2017 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”Architectures based on Recurrent Neural Networks (RNNs) have been successfully applied to many different tasks such as speech or handwriting recognition with state-of-The art results. The main contribution of this work is to analyse the feasibility of RNNs for on-line signature verification in real practical scenarios. We have considered a system based on Long Short-Term Memory (LSTM) with a Siamese architecture whose goal is to learn a similarity metric from pairs of signatures. For the experimental work, the BiosecurID database comprised of 400 users and 4 separated acquisition sessions are considered. Our proposed LSTM RNN system has outperformed the results of recent published works on the BiosecurID benchmark in figures ranging from 17.76% to 28.00% relative verification performance improvement for skilled forgeriesThis work has been supported by project TEC2015-70627-R MINECO/FEDER and by UAM-CecaBank Project. Ruben Tolosana is supported by a FPU Fellowship from Spanish MEC

GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning

Numerous studies in the literature have already shown the potential of biometrics on mobile devices for authentication purposes. However, it has been shown that, the learning processes associated to biometric systems might expose sensitive personal information about the subjects. This study proposes GaitPrivacyON, a novel mobile gait biometrics verification approach that provides accurate authentication results while preserving the sensitive information of the subject. It comprises two modules: i) two convolutional Autoencoders with shared weights that transform attributes of the biometric raw data, such as the gender or the activity being performed, into a new privacy-preserving representation; and ii) a mobile gait verification system based on the combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with a Siamese architecture. The main advantage of GaitPrivacyON is that the first module (convolutional Autoencoders) is trained in an unsupervised way, without specifying the sensitive attributes of the subject to protect. Two experimental studies have been examinated: i) MotionSense and MobiAct databases; and ii) OU-ISIR database. The experimental results achieved suggest the potential of GaitPrivacyON to significantly improve the privacy of the subject while keeping user authentication results higher than 96.6% Area Under the Curve (AUC). To the best of our knowledge, this is the first mobile gait verification approach that considers privacy-preserving methods trained in an unsupervised way

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

Predicting Humans’ Identity and Mental Load from EEG: Performed by AI

EEG-based brain machine/computer interfaces (BMIs/BCIs) have a wide range of clinical and non-clinical applications. Mental workload (MW) classification, emotion recognition, motor imagery, seizure detection, and sleep stage scoring are among the active BCI research areas. One of the relatively new BCI area is EEG-based human subject recognition (i.e., EEG biometric). There still exist several challenges that need to be addressed to design a successful EEG-based biometric model applicable for real-world environments. First, there is a need for a protocol that can elicit the individual dependent EEG responses in a short period of time. A classification algorithm with high generalization power is also required to deal with the EEG signals classification task. The latter is a common challenge for all EEG-based BCI paradigms; given the non-stationary nature of the EEG signals and the small size of the EEG datasets. In addition, to building a stable EEG biometric model, the effects of human mental states (e.g., emotion, mental load) on the model performance needs to be carefully examined. In this thesis, a new protocol for the area of the EEG biometric has been proposed. The proposed protocol called “(the) N-back task” is based on the human working memory and the experimental results obtained in this thesis prove that the EEG signals elicited by the N-back task contain subject specific features, even for very short time intervals. It has also been shown that three load levels of the typical N-back task are all capable of evoking subject specific EEG features. As a result, the N-back task can be used as a protocol having more than one mode (i.e, cancelable protocol) that comes with added security benefits. The EEG signals evoked by the N-back task have been used to train a compact convolutional neural network called the EEGNet. A configuration of the EEGNet having 16 temporal and 2 spatial filters has reached an identification accuracy of approximately 97% using data instances as short as 1.1s for a pool of 26 subjects. To further improve the accuracy, a novel ensemble classifier has been designed in this thesis. The principle underlying the proposed ensemble is the “division and exclusion” of the EEG channels guided by scalp locations. The ensemble classifier has (statistically significantly) improved the subject recognition rate from 97% to 99%. Performance of the proposed ensemble model has also been assessed in the EEG-based MW classification paradigm. The ensemble classifier outperformed the single EEGNet as well as a state-of-the-art classifier called WLnet in the challenging scenario of the subject-independent (cross-subject) MW classification. The results suggest that the ensemble structure proposed in this thesis can generalize to different BCI paradigms. Finally, effects of the mental workload on the performance of the EEG-based subject authentication models have been thoroughly explored in this thesis. The obtained results affirm that MW of the genuine and impostor subjects at the train and test phases have significant effects on both false negative rate (FNR) and false positive rate (FPR) of an authentication system. Different subjects have also shown different clusters of authentication behaviors when affected by the MW changes. This finding establishes the importance of the human’s mental load in the design of real-world EEG authentication systems and introduces a new investigation line for the EEG biometric community

Mobile Device Background Sensors: Authentication vs Privacy

The increasing number of mobile devices in recent years has caused the collection of a large amount of personal information that needs to be protected. To this aim, behavioural biometrics has become very popular. But, what is the discriminative power of mobile behavioural biometrics in real scenarios? With the success of Deep Learning (DL), architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM), have shown improvements compared to traditional machine learning methods. However, these DL architectures still have limitations that need to be addressed. In response, new DL architectures like Transformers have emerged. The question is, can these new Transformers outperform previous biometric approaches? To answers to these questions, this thesis focuses on behavioural biometric authentication with data acquired from mobile background sensors (i.e., accelerometers and gyroscopes). In addition, to the best of our knowledge, this is the first thesis that explores and proposes novel behavioural biometric systems based on Transformers, achieving state-of-the-art results in gait, swipe, and keystroke biometrics. The adoption of biometrics requires a balance between security and privacy. Biometric modalities provide a unique and inherently personal approach for authentication. Nevertheless, biometrics also give rise to concerns regarding the invasion of personal privacy. According to the General Data Protection Regulation (GDPR) introduced by the European Union, personal data such as biometric data are sensitive and must be used and protected properly. This thesis analyses the impact of sensitive data in the performance of biometric systems and proposes a novel unsupervised privacy-preserving approach. The research conducted in this thesis makes significant contributions, including: i) a comprehensive review of the privacy vulnerabilities of mobile device sensors, covering metrics for quantifying privacy in relation to sensitive data, along with protection methods for safeguarding sensitive information; ii) an analysis of authentication systems for behavioural biometrics on mobile devices (i.e., gait, swipe, and keystroke), being the first thesis that explores the potential of Transformers for behavioural biometrics, introducing novel architectures that outperform the state of the art; and iii) a novel privacy-preserving approach for mobile biometric gait verification using unsupervised learning techniques, ensuring the protection of sensitive data during the verification process

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System