Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Face recognition technologies for evidential evaluation of video traces

Human recognition from video traces is an important task in forensic investigations and evidence evaluations. Compared with other biometric traits, face is one of the most popularly used modalities for human recognition due to the fact that its collection is non-intrusive and requires less cooperation from the subjects. Moreover, face images taken at a long distance can still provide reasonable resolution, while most biometric modalities, such as iris and fingerprint, do not have this merit. In this chapter, we discuss automatic face recognition technologies for evidential evaluations of video traces. We first introduce the general concepts in both forensic and automatic face recognition , then analyse the difficulties in face recognition from videos . We summarise and categorise the approaches for handling different uncontrollable factors in difficult recognition conditions. Finally we discuss some challenges and trends in face recognition research in both forensics and biometrics . Given its merits tested in many deployed systems and great potential in other emerging applications, considerable research and development efforts are expected to be devoted in face recognition in the near future

On-line signature recognition through the combination of real dynamic data and synthetically generated static data

This is the author’s version of a work that was accepted for publication in Pattern Recognition . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition , 48, 9 (2005) DOI: 10.1016/j.patcog.2015.03.019On-line signature verification still remains a challenging task within biometrics. Due to their behavioral nature (opposed to anatomic biometric traits), signatures present a notable variability even between successive realizations. This leads to higher error rates than other largely used modalities such as iris or fingerprints and is one of the main reasons for the relatively slow deployment of this technology. As a step towards the improvement of signature recognition accuracy, the present paper explores and evaluates a novel approach that takes advantage of the performance boost that can be reached through the fusion of on-line and off-line signatures. In order to exploit the complementarity of the two modalities, we propose a method for the generation of enhanced synthetic static samples from on-line data. Such synthetic off-line signatures are used on a new on-line signature recognition architecture based on the combination of both types of data: real on-line samples and artificial off-line signatures synthesized from the real data. The new on-line recognition approach is evaluated on a public benchmark containing both real versions (on-line and off-line) of the exact same signatures. Different findings and conclusions are drawn regarding the discriminative power of on-line and off-line signatures and of their potential combination both in the random and skilled impostors scenarios.M. D.-C. is supported by a PhD fellowship from the ULPGC and M.G.-B. is supported by a FPU fellowship from the Spanish MECD. This work has been partially supported by projects: MCINN TEC2012-38630- C04-02, Bio-Shield (TEC2012-34881) from Spanish MINECO, BEAT (FP7-SEC-284989) from EU, CECABANK and Cátedra UAM-Telefónic

Gait Recognition: Databases, Representations, and Applications

There has been considerable progress in automatic recognition of people by the way they walk since its inception almost 20 years ago: there is now a plethora of technique and data which continue to show that a person’s walking is indeed unique. Gait recognition is a behavioural biometric which is available even at a distance from a camera when other biometrics may be occluded, obscured or suffering from insufficient image resolution (e.g. a blurred face image or a face image occluded by mask). Since gait recognition does not require subject cooperation due to its non-invasive capturing process, it is expected to be applied for criminal investigation from CCTV footages in public and private spaces. This article introduces current progress, a research background, and basic approaches for gait recognition in the first three sections, and two important aspects of gait recognition, the gait databases and gait feature representations are described in the following sections.Publicly available gait databases are essential for benchmarking individual approaches, and such databases should contain a sufficient number of subjects as well as covariate factors to realize statistically reliable performance evaluation and also robust gait recognition. Gait recognition researchers have therefore built such useful gait databases which incorporate subject diversities and/or rich covariate factors.Gait feature representation is also an important aspect for effective and efficient gait recognition. We describe the two main approaches to representation: model-free (appearance-based) approaches and model-based approaches. In particular, silhouette-based model-free approaches predominate in recent studies and many have been proposed and are described in detail.Performance evaluation results of such recent gait feature representations on two of the publicly available gait databases are reported: USF Human ID with rich covariate factors such as views, surface, bag, shoes, time elapse; and OU-ISIR LP with more than 4,000 subjects. Since gait recognition is suitable for criminal investigation applications of the gait recognition to forensics are addressed with real criminal cases in the application section. Finally, several open problems of the gait recognition are discussed to show future research avenues of the gait recognition

Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters

Data privacy is crucial when dealing with biometric data. Accounting for the latest European data privacy regulation and payment service directive, biometric template protection is essential for any commercial application. Ensuring unlinkability across biometric service operators, irreversibility of leaked encrypted templates, and renewability of e.g., voice models following the i-vector paradigm, biometric voice-based systems are prepared for the latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean and cosine comparators are known to ensure data privacy demands, without loss of discrimination nor calibration performance. Bridging gaps from template protection to speaker recognition, two architectures are proposed for the two-covariance comparator, serving as a generative model in this study. The first architecture preserves privacy of biometric data capture subjects. In the second architecture, model parameters of the comparator are encrypted as well, such that biometric service providers can supply the same comparison modules employing different key pairs to multiple biometric service operators. An experimental proof-of-concept and complexity analysis is carried out on the data from the 2013-2014 NIST i-vector machine learning challenge

Increasing the Robustness of Biometric Templates for Dynamic Signature Biometric Systems

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. R. Tolosana, R. Vera-Rodriguez, J. Ortega-Garcia and J. Fierrez, "Increasing the robustness of biometric templates for dynamic signature biometric systems," Security Technology (ICCST), 2015 International Carnahan Conference on, Taipei, 2015, pp. 229-234. doi: 10.1109/CCST.2015.7389687Due to the high deployment of devices such as smartphones and tablets and their increasing popularity in our society, the use of biometric traits in commercial and banking applications through these novel devices as an easy, quick and reliable way to perform payments is rapidly increasing. The handwritten signature is one of the most socially accepted biometric traits in these sectors due to the fact that it has been used in financial and legal transitions for centuries. In this paper we focus on dynamic signature verification systems. Nowadays, most of the state-of-the-art systems are based on extracting information contained in the X and Y spatial position coordinates of the signing process, which is stored in the biometric templates. However, it is critical to protect this sensible information of the users signatures against possible external attacks that would allow criminals to perform direct attacks to a biometric system or carry out high quality forgeries of the users signatures. Following this problem, the goal of this work is to study the performance of the system in two cases: first, an optimal time functions-based system taking into account the information related to X and Y coordinates and pressure, which is the common practice (i.e. Standard System). Second, we study an extreme case not considering information related to X, Y coordinates and their derivatives on the biometric system (i.e. Secure System), which would be a much more robust system against attacks, as this critical information would not be stored anywhere. The experimental work is carried out using e-BioSign database which makes use of 5 devices in total. The systems considered in this work are based on Dynamic Time Warping (DTW), an elastic measure over the selected time functions. Sequential Forward Features Selection (SFFS) is applied as a reliable way to obtain an optimal time functions vector over a development subset of users of the database. The results obtained over the evaluation subset of users of the database show a similar performance for both Standard and Secure Systems. Therefore, the use of a Secure System can be useful in some applications such as banking in order to avoid the lost of important user information against possible external attacks.This work was supported in part by the Project Bio-Shield (TEC2012-34881), in part by Cecabank e-BioFirma Contract, in part by the BEAT Project (FP7-SEC-284989) and in part by Catedra UAM-Telefonica

Digital evidence bags

This thesis analyses the traditional approach and methodology used to conduct digital forensic information capture, analysis and investigation. The predominant toolsets and utilities that are used and the features that they provide are reviewed. This is used to highlight the difficulties that are encountered due to both technological advances and the methodologies employed. It is suggested that these difficulties are compounded by the archaic methods and proprietary formats that are used. An alternative framework for the capture and storage of information used in digital forensics is defined named the `Digital Evidence Bag' (DEB). A DEB is a universal extensible container for the storage of digital information acquired from any digital source. The format of which can be manipulated to meet the requirements of the particular information that is to be stored. The format definition is extensible thereby allowing it to encompass new sources of data, cryptographic and compression algorithms and protocols as developed, whilst also providing the flexibility for some degree of backwards compatibility as the format develops. The DEB framework utilises terminology to define its various components that are analogous with evidence bags, tags and seals used for traditional physical evidence storage and continuity. This is crucial for ensuring that the functionality provided by each component is comprehensible by the general public, judiciary and law enforcement personnel without detracting or obscuring the evidential information contained within. Furthermore, information can be acquired from a dynamic or more traditional static environment and from a disparate range of digital devices. The flexibility of the DEB framework permits selective and/or intelligent acquisition methods to be employed together with enhanced provenance and continuity audit trails to be recorded. Evidential integrity is assured using accepted cryptographic techniques and algorithms. The DEB framework is implemented in a number of tool demonstrators and applied to a number of typical scenarios that illustrate the flexibility of the DEB framework and format. The DEB framework has also formed the basis of a patent application