Portrait of Dr Richard Guest

Dr Richard Guest

Reader in Biometric Systems Engineering
Deputy Head of School


Richard Guest is Reader of Biometric Systems Engineering and Deputy Head of School.

He has a significant involvement with biometrics standards development: as Panel Chair of BSI IST/44 Working Group 2 on Biometric Technical Interfaces and UK Principal Expert to ISO/IEC JTC1 SC37 in this area. He is also the Chair of the Training and Education Committee of the European Association of Biometrics (EAB) and a Fellow of the British Computer Society.

He has attracted over £2.7M over the past 10 years from external sources of funding including EPSRC, EU, ESRC, Leverhulme and industry and has been PI of 20 research separate grants. He has published over 100 peer reviewed articles and his research featured in the Government Select Committee Report into the Future of Biometrics and also in the Government Chief Scientist’s Annual Report 2015. 

He is the Project Coordinator for the AMBER EU Marie Skłodowska-Curie ITN in Mobile Biometrics (~€2.5M, 2017-2020) and is Kent PI on the EPSRC Hummingbird Project (2018-2019).

Research interests

Richard Guest has extensive research experience in the areas of image processing and pattern recognition specialising in hand-drawn data analysis for biometric, forensic and medical applications, biometric multimodality, system usability, standardisation issues, and sample quality and conformance. His current research work is exploring the data relationship between biometric and cyber-related data, including issues of sample quality and user interaction. He has published over 100 peer-reviewed publications. He has obtained research funding from, amongst other sources, EPSRC, the EU, charitable funds and industry.


Chartered Engineer


Showing 50 of 135 total publications in the Kent Academic Repository. View all publications.


  • Blanco-Gonzalo, R. et al. (2018). Biometrics: Accessibility challenge or opportunity? PlosOne [Online] 13:e0194111. Available at: https://doi.org/10.1371/journal.pone.0194111.
    Biometric recognition is currently implemented in several authentication contexts, most
    recently in mobile devices where it is expected to complement or even replace traditional
    authentication modalities such as PIN (Personal Identification Number) or passwords. The
    assumed convenience characteristics of biometrics are transparency, reliability and easeof-
    use, however, the question of whether biometric recognition is as intuitive and straightforward
    to use is open to debate. Can biometric systems make some tasks easier for people
    with accessibility concerns? To investigate this question, an accessibility evaluation of a
    mobile app was conducted where test subjects withdraw money from a fictitious ATM (Automated
    Teller Machine) scenario. The biometric authentication mechanisms used include
    face, voice, and fingerprint. Furthermore, we employed traditional modalities of PIN and pattern
    in order to check if biometric recognition is indeed a real improvement. The trial test subjects
    within this work were people with real-life accessibility concerns. A group of people
    without accessibility concerns also participated, providing a baseline performance. Experimental
    results are presented concerning performance, HCI (Human-Computer Interaction)
    and accessibility, grouped according to category of accessibility concern. Our results reveal
    links between individual modalities and user category establishing guidelines for future
    accessible biometric products.
  • Riggs, C. et al. (2017). The importance of search strategy for finding targets in open terrain. Cognitive Research: Principles and Implications [Online] 2. Available at: https://doi.org/10.1186/s41235-017-0049-4.
    A number of real-world search tasks (i.e. police search, detection of improvised explosive devices (IEDs)) require searchers to search exhaustively across open ground. In the present study, we simulated this problem by asking individuals (Experiments 1a and 1b) and dyads (Experiment 2) to search for coin targets pseudo-randomly located in a bounded area of open grassland terrain. In Experiment 1a, accuracy, search time, and the route used to search an area were measured. Participants tended to use an ‘S’-shaped pattern with a common width of search lane. Increased accuracy was associated with slower, but also variable, search speed, though only when participants moved along the length (as opposed to across the width) of the search area. Experiment 1b varied the number of targets available within the bounded search area and in doing so varied target prevalence and density. The results confirmed that the route taken in Experiment 1a generalizes across variations in target prevalence/density. In Experiment 2, accuracy, search time, and the search strategy used by dyads was measured. While dyads were more accurate than individuals, dyads that opted to conduct two independent searches were more accurate than those who opted to split the search space. The implications of these results for individuals and dyads when searching for targets in open space are discussed.
  • Guest, R. et al. (2017). Exploring the relationship between stride, stature and hand size for forensic assessment. Journal of Forensic and Legal Medicine [Online] 52:46-55. Available at: http://dx.doi.org/10.1016/j.jflm.2017.08.006.
    Forensic evidence often relies on a combination of accurately recorded measurements, estimated measurements from landmark data such as a subject's stature given a known measurement within an image, and inferred data. In this study a novel dataset is used to explore linkages between hand measurements, stature, leg length and stride. These three measurements replicate the type of evidence found in surveillance videos with stride being extracted from an automated gait analysis system. Through correlations and regression modelling, it is possible to generate accurate predictions of stature from hand size, leg length and stride length (and vice versa), and to predict leg and stride length from hand size with, or without, stature as an intermediary variable. The study also shows improved accuracy when a subject's sex is known a-priori. Our method and models indicate the possibility of calculating or checking relationships between a suspect's physical measurements, particularly when only one component is captured as an accurately recorded measurement.
  • Guest, R. et al. (2016). An assessment of the usability of biometric signature systems using the human-biometric sensor interaction model’. International Journal of Computer Applications in Technology [Online] 53:336-347. Available at: http://dx.doi.org/10.1504/IJCAT.2016.076810.
    Signature biometrics is a widely used form of user authentication. As a behavioural biometric, samples have inherent inconsistencies which must be accounted for within an automated system. Performance deterioration of a tuned biometric software system may be caused by an interaction error with a biometric capture device, however, using conventional error metrics, system and user interaction errors are combined, thereby masking the contribution by each element. In this paper we explore the application of the Human-Biometric Sensor Interaction (HBSI) model to signature as an exemplar of a behavioural biometric. Using observational data collected from a range of subjects, our study shows that usability issues can be identified specific to individual capture device technologies. While most interactions are successful, a range of common interaction errors need to be mitigated by design to reduce overall error rates.
  • Miguel-Hurtado, O. et al. (2016). Predicting sex as a soft-biometrics from device interaction swipe gestures. Pattern Recognition Letters [Online] 79:44-51. Available at: http://www.dx.doi.org/10.1016/j.patrec.2016.04.024.
    Touch and multi-touch gestures are becoming the most common way to interact with technology such as smart phones, tablets and other mobile devices. The latest touch-screen input capacities have tremendously increased the quantity and quality of available gesture data, which has led to the exploration of its use in multiple disciplines from psychology to biometrics. Following research studies undertaken in similar modalities such as keystroke and mouse usage biometrics, the present work proposes the use of swipe gesture data for the prediction of soft-biometrics, specifically the user's sex. This paper details the software and protocol used for the data collection, the feature set extracted and subsequent machine learning analysis. Within this analysis, the BestFirst feature selection technique and classification algorithms (naïve Bayes, logistic regression, support vector machine and decision tree) have been tested. The results of this exploratory analysis have confirmed the possibility of sex prediction from the swipe gesture data, obtaining an encouraging 78% accuracy rate using swipe gesture data from two different directions. These results will hopefully encourage further research in this area, where the prediction of soft-biometrics traits from swipe gesture data can play an important role in enhancing the authentication processes based on touch-screen devices.
  • Robertson, J. et al. (2016). A Framework for Biometric and Interaction Performance Assessment of Automated Border Control Processes. IEEE Transactions on Human-Machine Systems [Online] 47:983-993. Available at: http://doi.org/10.1109/THMS.2016.2611822.
    Automated Border Control (ABC) in airports and land crossings utilize automated technology to verify passenger identity claims. Accuracy, interaction stability, user error, and the need for a harmonized approach to implementation are required. Two models proposed in this paper establish a global path through ABC processes. The first, the generic model, maps separately the enrolment and verification phases of an ABC scenario. This allows a standardization of the process and an exploration of variances and similarities between configurations across implementations. The second, the identity claim process, decomposes the verification phase of the generic model to an enhanced resolution of ABC implementations. Harnessing a human-biometric sensor interaction framework allows the identification and quantification of errors within the system's use, attributing these errors to either system performance or human interaction. Data from a live operational scenario are used to analyze behaviors, which aid in establishing what effect these have on system performance. Utilizing the proposed method will aid already established methods in improving the performance assessment of a system. Through analyzing interactions and possible behavioral scenarios from the live trial, it was observed that 30.96% of interactions included some major user error. Future development using our proposed framework will see technological advances for biometric systems that are able to categorize interaction errors and feedback appropriately.
  • Stevenage, S. and Guest, R. (2016). Combining Forces: Data fusion across man and machine for biometric analysis. Image and Vision Computing [Online]. Available at: http://doi.org/10.1016/j.imavis.2016.03.012.
    Through the HUMMINGBIRD framework outlined here,we seek to encourage a novel multidisciplinary approach to biometric analysis with the goal of enhancing both understanding and accuracy of identification.
  • Liang, Y. et al. (2016). Automatic Handwriting Feature Extraction, Analysis and Visualization in the Context of Digital Palaeography. International Journal of Pattern Recognition and Artificial Intelligence [Online] 30:1653001. Available at: http://doi.org/10.1142/S0218001416530013.
    Digital palaeography is an emerging research area which aims to introduce digital image processing techniques into palaeographic analysis for the purpose of providing objective quantitative measurements. This paper explores the use of a fully automated handwriting feature extraction, visualization, and analysis system for digital palaeography which bridges the gap between traditional and digital palaeography in terms of the deployment of feature extraction techniques and handwriting metrics. We propose the application of a set of features, more closely related to conventional palaeographic assesment metrics than those commonly adopted in automatic writer identification. These features are emprically tested on two datasets in order to assess their effectiveness for automatic writer identification and aid attribution of individual handwriting characteristics in historical manuscripts. Finally, we introduce tools to support visualization of the extracted features in a comparative way, showing how they can best be exploited in the implementation of a content-based image retrieval (CBIR) system for digital archiving.

    Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001416530013
  • Miguel-Hurtado, O. et al. (2016). Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics. PLOS ONE [Online] 11:e0165521. Available at: http://doi.org/10.1371/journal.pone.0165521.
    Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.
  • Riggs, C. et al. (2015). The Importance of Slow Consistent Movement when Searching for Hard-to-Find Targets in Real-World Visual Search. Journal of Vision [Online] 15:1355. Available at: http://doi.org/10.1167/15.12.1355.
    Various real-world tasks require careful and exhaustive visual search. For example, searching for forensic evidence or signs of hidden threats (what we call hard-to-find targets). Here, we examine how search accuracy for hard-to-find targets is influenced by search behaviour. Participants searched for coins set amongst a 5m x 15m (defined as x and y axes respectively) piece of grassland. The grassland contained natural distractors of leaves and flowers and was not manicured. Coins were visually detectable from standing height. There was no time limit to the task and participants were instructed to search until they were confident they had completed their search. On average, participants detected 45% (SD=23%) of the targets and took 7:23 (SD=4:44) minutes to complete their search. Participants' movement over space and time was recorded as a series of time-stamped x, y coordinates using a Total Station theodolite. To quantify their search behaviour, the x- and y-coordinates of participants' physical locations as they searched the grassland were converted into the frequency domain using a Fourier transform. Decreases in dominant frequencies, a measure of the time before turning during search, resulted in increased response accuracy as well as increased search times. Furthermore, decreases in the number of iterations, defined by the total search time divided by the dominant frequency, also resulted in increased accuracy and search times. Comparing distance between the two most dominant frequency peaks provided a measure of consistency of movement over time. This measure showed that more variable search was associated with slower search times but no improvement in accuracy. Throughout our analyses, these results were true for the y-axis but not the x-axis. At least with respect to the present task, accurate search for hard-to-find targets is dependent on conducting search at a slow consistent speed where changes in direction are minimised. Meeting abstract presented at VSS 2015.
  • Morton, A. et al. (2015). Signature forgery and the forger – an assessment of influence on handwritten signature production. IT in Industry 3:54-58.
    Signatures are widely used as a form of personal authentication. Despite ubiquity in deployment, individual signatures are relatively easy to forge, especially when only the static ‘pictorial’ outcome of the signature is considered at verification time. In this study, we explore opinions on signature usage for verification purposes, and how individuals rate a particular third-party signature in terms of ease of forgeability and their own ability to forge. We examine responses with respect to an individual’s experience of the forgeability/complexity of their own signature. Our study shows that past experience does not generally have an effect on perceived signature complexity nor the perceived effectiveness of an individual to themselves forge a signature. In assessing forgeability, most subjects cite the overall signature complexity and distinguishing features in reaching this decision. Furthermore, our research indicates that individuals typically vary their signature according to the scenario but generally little effort into the production of the signature.
  • Robertson, J. and Guest, R. (2015). A feature based comparison of pen and swipe based signature characteristics. Human Movement Science [Online] 43:169-182. Available at: http://doi.org/10.1016/j.humov.2015.06.003.
    Dynamic Signature Verification (DSV) is a biometric modality that identifies anatomical and behavioral characteristics when an individual signs their name. Conventionally signature data has been captured using pen/tablet apparatus. However, the use of other devices such as the touch-screen tablets has expanded in recent years affording the possibility of assessing biometric interaction on this new technology.

    To explore the potential of employing DSV techniques when a user signs or swipes with their finger, we report a study to correlate pen and finger generated features. Investigating the stability and correlation between a set of characteristic features recorded in participant’s signatures and touch-based swipe gestures, a statistical analysis was conducted to assess consistency between capture scenarios.

    The results indicate that there is a range of static and dynamic features such as the rate of jerk, size, duration and the distance the pen traveled that can lead to interoperability between these two systems for input methods for use within a potential biometric context. It can be concluded that this data indicates that a general principle is that the same underlying constructional mechanisms are evident.
  • Tabatabaey-Mashadi, N. et al. (2014). Analyses of pupils’ polygonal shape drawing strategy with respect to handwriting performance. Pattern Analysis and Applications [Online]:1-16. Available at: http://doi.org/10.1007/s10044-014-0423-5.
    Polygonal shape drawing tasks are commonly used in psychological, clinical and standard handwriting tests to evaluate children’s development. Early detection of physical/mental disorders within subjects therefore requires objective analysis of the drawing tasks. This analysis would help to identify specific rehabilitation needs and accurate detection of disorders. Herein, the aim is to determine the correlation between the performance of polygonal shape drawing and levels in handwriting performance. In the reported experimentation two groups of participants aged between 6 and 7 were studied. The first group was identified by educational experts as being below-average writers within their age group whilst the second group was age-matched controls of average and above. Subjects were required to draw an isosceles triangle within a novel computer-based framework founded on a pen-based graphic tablet capture device. Subsequently, a sequential feature vector containing performance values relating to the order in which they drew the triangle was extracted from tablet data and compared against one another when presented in constructional strategy models. Statistical analyses and automated classification were applied to sequences to infer handwriting level based on the triangle drawing strategy. From our experiments drawing strategies showed significant differences in drawing end-point position, number of strokes used, and the frequency of particular drawing strategies amongst average and below-average handwriting groups. Additionally, a support vector machine classifier was used to detect group membership based on the triangle drawing strategy. From this exemplar polygonal shape drawing study it is revealed that there are details in children’s drawing strategy which considerably differs in grouping based on handwriting performance.
  • Guest, R., Hurtado, O. and Henniger, O. (2014). Assessment of methods for image recreation for signature time-series data. IET Biometrics [Online] 3:159-166. Available at: http://dx.doi.org/10.1049/iet-bmt.2013.0022.
    Human signatures are widely used for biometric authentication. For automatic online signature verification, rather than storing an image of the completed signature, data are represented in the form of a time series of pen position and status information allowing the extraction of temporal-based features. For visualisation purposes, signature images need to be recreated from time-series data. In this study, the authors investigate the accuracy and verification performance of a series of interpolation methods for recreating a signature image from the time-series data contained in two ISO/IEC data storage formats. The authors experiments investigate dynamic data stored at various sample rates and signature images recreated at differing resolutions. Their study indicates possible best practice in terms of image recreation method, recreated image resolution and temporal sample rate and assesses the effect on the accuracy of reconstructed signature data.
  • Liang, Y. et al. (2014). EMMEL: a framework for historical manuscript analysis and presentation. Universal Access in the Information Society [online] 13:147-160. Available at: http://dx.doi.org/10.1007/s10209-013-0298-z.
    In this paper, a generic framework for historical manuscript image and data processing, visualisation and analysis is introduced with a focus on the modelling of manuscript metadata underpinning the interaction. The goal of such a framework is to capture the requirements from three types of activities involving historical manuscripts: presentation, management and analysis. In addition to an overall text-based description of an historical manuscript, a central requirement of such a framework is to associate rich media information (e.g. video, flash component, etc.) to the manuscript or a specific region of the manuscript. A second requirement is to enable interchange of the manuscript data as well as the attached information between users. As a result of an extensive analysis of requirements collected across a wide range of target user groups, an XML-based metadata language derived from a relational database model is proposed to form an historical document data model, and a prototype system is developed to demonstrate some of the advanced functionalities enabled by this data model. Thus, the proposed framework provides an important tool in promoting access to historical documents on a wide and diverse basis, embracing the fundamental principles of universal access to a shared cultural heritage.

Book section

  • Henniger, O. et al. (2015). Signature/Sign Time Series Data:Standardization. in: Encyclopedia of Biometrics. Springer US, pp. 1-9. Available at: http://doi.org/10.1007/978-3-642-27733-7_9125-2.
  • Brockly, M. et al. (2015). Human Biometric Sensor Interaction. in: Encyclopedia of Biometrics, 2nd Edition. Springer. Available at: http://doi.org/10.1007/978-3-642-27733-7_2261-3.

Conference or workshop item

  • Tolosana, R. et al. (2018). Complexity-based Biometric Signature Verification. in: 14th IAPR International Conference on Document Analysis and Recognition. IEEE. Available at: https://doi.org/10.1109/ICDAR.2017.40.
    On-line signature verification systems are mainly based on two approaches: feature- or time functions-based systems (a.k.a. global and local systems). However, new sources of information can be also considered in order to complement these traditional approaches, reduce the intra-class variability and achieve more robust signature verification systems against forgers. In this paper we focus on the use of the concept of complexity in on-line signature verification systems. The main contributions of the present work are: 1) classification of users according to the complexity level of their signatures using features extracted from the Sigma LogNormal writing generation model, and 2) a new architecture for signature verification exploiting signature complexity that results in highly improved performance. Our proposed approach is tested considering the BiosecurID on-line signature database with a total of 400 users. Results of 5.8% FRR for a FAR = 5.0% have been achieved against skilled forgeries outperforming recent related works. In addition, an analysis of the optimal time functions for each complexity level is performed providing practical insights for the application of signature verification in real scenarios.
  • Ellavarason, E., Guest, R. and Deravi, F. (2018). A Framework for Assessing Factors Influencing User Interaction for Touch-based Biometrics. in: 26th European Signal Processing Conference (Eusipco 2018). USA: IEEE. Available at: http://dx.doi.org/10.23919/EUSIPCO.2018.8553537.
    Touch-based behavioural biometrics is an
    emerging technique for passive and transparent user
    authentication on mobile devices. It utilises dynamics mined
    from users’ touch actions to model behaviour. The
    interaction of the user with the mobile device using touch is
    an important aspect to investigate as the interaction errors
    can influence the stability of sample donation and overall
    performance of the implemented biometric authentication
    system. In this paper, we are outlining a data collection
    framework for touch-based behavioural biometric
    modalities (signature, swipe and keystroke dynamics) that
    will enable us to study the influence of environmental
    conditions and body movement on the touch-interaction. In
    order to achieve this, we have designed a multi-modal
    behavioural biometric data capturing application
    “Touchlogger” that logs touch actions exhibited by the user
    on the mobile device. The novelty of our framework lies in
    the collection of users’ touch data under various usage
    scenarios and environmental conditions. We aim to collect
    touch data in two different environments - indoors and
    outdoors, along with different usage scenarios - whilst the
    user is seated at a desk, walking on a treadmill, walking
    outdoors and seated on a bus. The range of collected data
    may include swiping, signatures using finger and stylus,
    alphabetic, numeric keystroke data and writing patterns
    using a stylus.
  • Guest, R., Miguel-Hurtado, O. and Chatzisterkotis, T. (2017). A New Approach to Automatic Signature Complexity Assessment. in: IEEE International Carnahan Conference on Security Technology. IEEE. Available at: https://doi.org/10.1109/CCST.2016.7815678.
    Understanding signature complexity has been shown to be a crucial facet for both forensic and biometric appbcations. The signature complexity can be defined as the difficulty that forgers have when imitating the dynamics (constructional aspects) of other users signatures. Knowledge of complexity along with others facets such stability and signature length can lead to more robust and secure automatic signature verification systems. The work presented in this paper investigates the creation of a novel mathematical model for the automatic assessment of the signature complexity, analysing a wider set of dynamic signature features and also incorporating a new layer of detail, investigating the complexity of individual signature strokes. To demonstrate the effectiveness of the model this work will attempt to reproduce the signature complexity assessment made by experienced FDEs on a dataset of 150 signature samples.
  • Miguel-Hurtado, O. et al. (2017). Interaction evaluation of a mobile voice authentication system. in: IEEE International Carnahan Conference on Security Technology. IEEE. Available at: https://doi.org/10.1109/CCST.2016.7815697.
    Biometric recognition is nowadays widely used in smartphones, making the users' authentication easier and more transparent than PIN codes or patterns. Starting from this idea, the EU project PIDaaS aims to create a secure authentication system through mobile devices based on voice and face recognition as two of the most reliable and user-accepted modalities. This work introduces the project and the first PIDaaS usability evaluation carried out by means of the well-known HBSI model In this experiment, participants interact with a mobile device using the PIDaaS system under laboratory conditions: video recorded and assisted by an operator. Our findings suggest variability among sessions in terms of usability and feed the next PIDaaS HCI design.
  • Alsedais, R. and Guest, R. (2017). Person re-identification from CCTV silhouettes using Generic Fourier Descriptors. in: 51st IEEE International Carnahan Conference on Security Technology. Institute of Electrical and Electronics Engineers.
    Person re-identification in public areas (such as airports, train stations and shopping malls) has recently received increased attention from computer vision researchers due, in part, to the demand for enhanced levels of security. Reidentifying subjects within non-overlapped camera networks can be considered as a challenging task. Illumination changes in different scenes, variations in camera resolutions, field of view and human natural motion are the key obstacles to accurate implementation. This study assesses the use of Generic Fourier Shape Descriptor (GFD) on person silhouettes for reidentification and further established which sections of a subject’s silhouette is able to deliver optimum performance. Human silhouettes of 90 subjects from the CASIA dataset walking 0° and 90° to a fixed CCTV camera were used for the purpose of re-identification. Each subject’s video sequence comprised between 10 and 50 frames. For both views, silhouettes were segmented into eight algorithmically defined areas: head and neck, shoulders, upper 50%, lower 50%, upper 15%, middle 35%, lower 40% and whole body. A GFD was used independently on each segment at each angle. After extracting the GFD feature for each frame, a linear discriminant analysis (LDA) classifier was used to investigate re-identification accuracy rate, where 50% of each subject’s frames were training and the other 50% were testing. The results show that 97% identification accuracy rate at the 10th rank is achieved by using GFD on the upper 50% segment of the human silhouette front (0°) side. From 90° images, using GFD on the upper 15% silhouette segment was almost 98% accuracy rate at the 10th rank. This study illustrates which segments
  • Lunerti, C. et al. (2017). Environmental Effects on Face Recognition in Smartphones. in: 51st IEEE International Carnahan Conference on Security Technology. Institute of Electrical and Electronics Engineers.
    Face recognition is convenient for user authentication on smartphones as it offers several advantages suitable for mobile environments. There is no need to remember a numeric code or password or carry tokens. Face verification allows the unlocking of the smartphone, pay bills or check emails through looking at the smartphone. However, devices mobility also introduces a lot of factors that may influence the biometric performance mainly regarding interaction and environment. Scenarios can vary significantly as there is no control of the surroundings. Noise can be caused by other people appearing on the background, by different illumination conditions, by different users’ poses and through many other reasons. User-interaction with biometric systems is fundamental: bad experiences may derive to unwillingness to use the technology. But how does the environment influence the quality of facial images? And does it influence the user experience with face recognition? In order to answer these questions, our research investigates the user-biometric system interaction from a non-traditional point of view: we recreate reallife scenarios to test which factors influence the image quality in face recognition and, quantifiably, to what extent. Results indicate the variability in face recognition performance when varying environmental conditions using smartphones.
  • Miguel-Hurtado, O., Guest, R. and Lunerti, C. (2017). Voice and face interaction evaluation of a mobile authentication platform. in: 51st IEEE International Carnahan Conference on Security Technology. Institute of Electrical and Electronics Engineers.
    Biometric authentication in mobile devices has become a key aspect of application security. However, the use of dedicated sensors such as fingerprint/iris sensors may not always be feasible. As an alternative, the use of face and voice biometrics using the generic sensors integrated in smartphones is gaining momentum. This work applied the HBSI framework to analyise the user’s interaction with the mobile PIDaaS platform that integrates voice and face authentication. Our analysis enables a thorough comparison between the user’s interaction for these two modalities with the same population.
  • Brockly, M. et al. (2017). The development of a test harness for biometric data collection and validation. in: IEEE International Carnahan Conference on Security Technology. IEEE. Available at: https://doi.org/10.1109/CCST.2016.7815696.
    Biometric test reports are an important tool in the evaluation of biometric systems, and therefore the data entered into the system needs to be of the highest integrity. Data collection, especially across multiple modalities, can be a challenging experience for test administrators. They have to ensure that the data are collected properly, the test subjects are treated appropriately, and the test plan is followed. Tests become more complex as the number of sensors are increased, and therefore it becomes increasingly important that a test harness be developed to improve the accuracy of the data collection. This paper describes the development of a test harness for a complex multi-sensor, multi-visit data collection, and explains the processes for the development of such a harness. The applicability of such a software package for the broader biometric community is also considered.
  • Elliot, S. and Guest, R. (2016). Human Biometric Sensor Interaction, latest research and Process HBSI. in: International Biometric Performance Testing Conference 2016,.
  • Miguel-Hurtado, O. and Guest, R. (2016). Users-Centric Design: introducing remote usability evaluation in mobile implementations. in: International Biometric Performance Testing Conference 2016,.
  • Guest, R. and Miguel-Hurtado, O. (2016). User-Interaction Evaluation of a Mobile Authentication System. in: EAB Research Projects Conference (EAB-RPC) 2016.
  • Elliott, S. et al. (2015). Expanding the human-biometric sensor interaction model to identify claim scenarios. in: International Conference of Identity, Security and Behaviour Analysis.
  • Guest, R. and Hurtado, O. (2015). Mobile Biometric Usability Assessment within PIDaaS. in: EAB Research Projects Conference (EAB-RPC) 2015.
  • Riggs, C. et al. (2015). The Importance of Slow Consistent Movement when Searching for Hard-to-Find Targets in Real-World Visual Search. in: Proc: Vision Sciences Society, 15th Annual Meeting.
  • Morton, A. et al. (2015). Signature forgery and the forger – an assessment of influence on handwritten signature production. in: Proceedings ICITA 2015.. Available at: http://www.icita.org/2015/abstracts/us-morton.htm.
    Signatures are widely used as a form of personal authentication. Despite ubiquity in deployment, individual signatures are relatively easy to forge, especially when only the static ‘pictorial’ outcome of the signature is considered at verification time. In this study we explore opinions on signature usage for verification purposes, and how individuals rate a particular third-party signature in terms of ease of forgeability and their own ability to forge. We examine responses with respect to an individual’s experience of the forgeability/complexity of their own signature. Our study shows that past experience does not generally have an effect on perceived signature complexity nor the perceived effectiveness of an individual to themselves forge a signature. In assessing forgeability, most subjects cite overall signature complexity and distinguishing features in reaching this decision. Furthermore, our research indicates that individuals typically vary their signature according to the scenario but generally little effort into the production of the signature.
  • Elliott, S. et al. (2015). Expanding the human-biometric sensor interaction model to identity claim scenarios. in: 2015 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA). pp. 1-6. Available at: http://doi.org/10.1109/ISBA.2015.7126362.
    Biometric technologies represent a significant component of comprehensive digital identity solutions, and play an important role in crucial security tasks. These technologies support identification and authentication of individuals based on their physiological and behavioral characteristics. This has led many governmental agencies to choose biometrics as a supplement to existing identification schemes, most prominently ID cards and passports. Studies have shown that the success of biometric systems relies, in part, on how humans interact and accept such systems. In this paper, the authors build on previous work related to the Human-Biometric Sensor Interaction (HBSI) model and examine it with respect to the introduction of a token (e.g. an electronic passport or identity card) into the biometric system. The role of the imposter within an Identity Claim scenario has been integrated to expand the HBSI model into a full version, which is able to categorise potential False Claims and Attack Presentations.
  • Felipe, L., De Oliveira, B. and Guest, R. (2015). An Assessment of Dynamic Signature Forgery Creation Methodology and Accuracy. in: 17th International Graphonomics Society Conference.
  • Brockly, M. et al. (2014). An investigation into biometric signature capture device performance and user acceptance. in: International Carnahan Conference onSecurity Technology (ICCST), 2014. IEEE, pp. 1-5. Available at: http://dx.doi.org/10.1109/CCST.2014.6986970.
    The human signature provides a natural and publically-accepted legally-admissible method for providing authentication to a process. Automatic biometric signature systems assess both the drawn image and the temporal aspects of signature construction, providing enhanced verification rates over and above conventional outcome assessment. To enable the capture of these constructional data requires the use of specialist `tablet' devices. In this paper we explore the enrolment performance using a range of common signature capture devices and investigate the reasons behind user preference. The results show that writing feedback and familiarity with conventional `paper and pen' donation configurations are the primary motivation for user preference. These results inform the choice of signature device from both technical performance and user acceptance viewpoints.
  • Hurtado, O. et al. (2014). The relationship between handwritten signature production and personality traits. in: International Joint Conference on Biometrics.
  • Guest, R. et al. (2014). Biometrics within the superidentity project: a new approach to spanning multiple identity domains. in: IEEE International Carnahan Conference on Security Technology (ICCST).
  • Tabatabaey-Mashadi, N. et al. (2013). Automatically measuring the effect of strategy drawing features on pupils’ handwriting and gender. in: ICMV 2013.
  • Guest, R. and Henniger, O. (2013). Assessment of the quality of handwritten signatures based on multiple correlations. in: Proceedings of International Conference on Biometrics. pp. 1-6. Available at: http://dx.doi.org/10.1109/ICB.2013.6613011.
  • Guest, R. et al. (2013). An Assessment of the Human Performance of Iris Identification. in: IEEE HST 2013.
  • Guest, R. and He, H. (2013). A Configurable Multi-Engine System Based on Performance Matrices for Face Recognition. in: IEEE HST 2013.

Datasets / databases

  • Miguel-Hurtado, O. et al. (2016). Hand images and lengths dataset: Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics. [Website]. Available at: http://dx.doi.org/10.5281/zenodo.17487.
    The zip contains right and left hand geometry images from 112 participants. The images were captured using a Nikon D200 SLR camera (format: jpg, size: 3504x2336 pixels), with both the palm of the hand and camera facing downwards. Participants placed each hand on an acetate sheet with a series of positioning pegs.


    The excel contains a series of length measurements (based on the underlying skeleton of the hand) manually extracted (see Figure 1 for details) along with demographic information from the participants: sex (male or female), height (in cm), weight (in kg) and foot size (in UK sizes).


  • Guest, R. (2016). ISO/IEC JTC1 SC37 30106-3:OO BioAPI Part 3:2016. International Standards Organization.
  • Guest, R. (2014). Information technology -- Biometric data interchange formats -- 19794-Part 7: Signature/sign time series data - Second Edition. ISO/IEC.
    ISO/IEC 19794-7:2014 specifies data interchange formats for signature/sign behavioural data captured in the form of a multi-dimensional time series using devices such as digitizing tablets or advanced pen systems. The data interchange formats are generic, in that they may be applied and used in a wide range of application areas where handwritten signs or signatures are involved. No application-specific requirements or features are addressed in ISO/IEC 19794-7:2014.
  • Guest, R. (2013). Information Technology- Biometric data interchange formats 19794 - 11: Signature/sign processed dynamic data. ISO/IEC. Available at: http://www.iso.org/iso/catalogue_detail.htm?csnumber=51824.
    For the purpose of biometric comparison, ISO/IEC 19794-11:2013 specifies a data interchange format for processed signature/sign behavioural data extracted from a time series, captured using devices such as digitizing tablets, pen-based computing devices, or advanced pen systems.

    The data interchange format is generic, in that it may be applied and used in a wide range of application areas where handwritten signs or signature/signs are involved. No application-specific requirements or features are addressed in ISO/IEC 19794-11:2013.

    ISO/IEC 19794-11:2013 contains definitions of relevant terms, a description of what data is extracted, and a data format for containing the data, together with advice on whether a set of user's signature/sign is suitable for identification purposes using ISO/IEC 19794-11:2013.

    It is advisable that stored and transmitted biometric data is time-stamped and that cryptographic techniques be used to protect their authenticity, integrity, and confidentiality; however, such provisions are beyond the scope of ISO/IEC 19794-11:2013.


  • Guest, R. (2014). 19794-7/PDAM 1 Information technology -- Biometric data interchange formats -- Part 7: Signature/sign time series data -- Amendment 1: XML encoding. [International Standard].
  • Guest, R. (2013). 19794-11 Information technology -- Biometric Data Interchange Formats -- Part 11: Signature/sign processed dynamic data. [International Standard].


  • Guest, R. et al. (2018). Sensing Movement on Smartphone Devices to Assess User Interaction for Face Verification. in: IEEE ICCST 2018, Montreal, Canada. USA: IEEE.
    Unlocking and protecting smartphone devices has
    become easier with the introduction of biometric face verification,
    as it has the promise of a secure and quick authentication solution
    to prevent unauthorised access. However, there are still many
    challenges for this biometric modality in a mobile context, where
    the user’s posture and capture device are not constrained. This
    research proposes a method to assess user interaction by analysing
    sensor data collected in the background of smartphone devices
    during verification sample capture. From accelerometer data, we
    have extracted magnitude variations and angular acceleration for
    pitch, roll, and yaw (angles around the x-axis, y-axis, and z-axis of
    the smartphone respectively) as features to describe the amplitude
    and number of movements during a facial image capture process.
    Results obtained from this experiment demonstrate that it can be
    possible to ensure good sample quality and high biometric
    performance by applying an appropriate threshold that will
    regulate the amplitude on variations of the smartphone
    movements during facial image capture. Moreover, the results
    suggest that better quality images are obtained when users spend
    more time positioning the smartphone before taking an image.
  • Gonzalo, B. et al. (2018). Attacking a smartphone biometric fingerprint system:a novice’s approach. in: IEEE ICCST 2018, Montreal, Canada. USA: IEEE.
    Biometric systems on mobile devices are an
    increasingly ubiquitous method for identity verification. The
    majority of contemporary devices have an embedded fingerprint
    sensor which may be used for a variety of transactions including
    unlock a device or sanction a payment. In this study we explore
    how easy it is to successfully attack a fingerprint system using a
    fake finger manufactured from commonly available materials.
    Importantly our attackers were novices to producing the fingers
    and were also constrained by time. Our study shows the relative
    ease that modern devices can be attacked and the material
    combinations that lead to these attacks.
  • Eastwood, S. et al. (2018). Technology Gap Navigator: Emerging Design of Biometric-Enabled Risk Assessment Machines. in: BIOSIG 2018 - 17th International Conference of the Biometrics Special Interest Group. IEEE.
    This paper reports the Technology Gap (TG) navigator, a novel tool for individual risk assessment
    in the layered security infrastructure. It is motivated by the practical need of the biometricenabled
    security systems design. The tool helps specify the conditions for bridging the identified
    TGs. The input data for the TG navigator includes 1) a causal description of the TG, 2) statistics
    regarding the available resources and performances, and 3) the required performance. The output includes
    generated probabilistic conditions, and the corresponding technology requirements for bridging
    the targeted TG.