Abstract: Finger image quality assessment is a crucial part of any system where a high biometric performance and user satisfaction is desired. Several algorithms measuring selected aspects of finger image quality have been proposed in the literature, yet only few of them have found their way into quality assessment algorithms used in practice. The authors provide comprehensive algorithm descriptions and make available implementations of adaptations of ten quality assessment algorithms from the literature which operates at the local or the global image level. They evaluate the performance on four datasets in terms of the capability in determining samples causing false non-matches and by their Spearman correlation with sample utility. The authors' evaluation shows that both the capability in rejecting samples causing false non-matches and the correlation between features varies depending on the dataset.

Abstract: The vulnerability of biometric systems to external attacks using a physical artefact in order to impersonate the legitimate user has become a major concern over the last decade. Such a threat, commonly known as ‘spoofing’, poses a serious risk to the integrity of biometric systems. The usual low-complexity and low-cost characteristics of these attacks make them accessible to the general public, rendering each user a potential intruder. The present study addresses the spoofing issue analysing the feasibility to perform low-cost attacks with self-manufactured three-dimensional (3D) printed models to 2.5D and 3D face recognition systems. A new database with 2D, 2.5D and 3D real and fake data from 26 subjects was acquired for the experiments. Results showed the high vulnerability of the three tested systems, including a commercial solution, to the attacks.

Abstract: Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes.

Abstract: Nowadays, biometrics-based authentication is playing a potential approach for many modern applications such as banking, homeland security etc. However, the end-users may feel uncomfortable to deploy this technology because of not well-solved accurate rate and security problems. To overcome these issues, some significant techniques have been proposed such as biometric template protection, reducing biometric extraction noise etc. Fuzzy vault is one of the most popular methods for biometric template security, which binds a secret key with biometric features and produces one kind of data, called the helper data, for recovering the secret key. Unfortunately, the major drawback of this approach is the lacking of cancellable property. Furthermore, most of the fuzzy vault schemes are performed on two biometrics modalities: fingerprints and iris. Some techniques were introduced to transform the original biometric feature to cancellable one. However, the computational cost of these proposals was quite large. In this research, the authors introduce a periodic transformation attached to fuzzy vault to produce the new cancellable scheme. Their transformation is not only simpler but also suitable for many kinds of biometrics modalities. The experiments demonstrate that this approach is practical with a little better error rate in comparison with the original biometric feature.

Abstract: The ‘fuzzy vault scheme’ is a cryptographic primitive being considered for storing fingerprint minutiae protected. A well-known problem of the fuzzy vault scheme is its vulnerability against correlation attack-based cross-matching thereby conflicting with the ‘unlinkability requirement’ and ‘irreversibility requirement’ of effective biometric information protection. Yet, it has been demonstrated that in principle a minutiae-based fuzzy vault can be secured against the correlation attack by passing the to-be-protected minutiae through a quantisation scheme. Unfortunately, single fingerprints seem not to be capable of providing an acceptable security level against offline attacks. To overcome the aforementioned security issues, this study shows how an implementation for multiple fingerprints can be derived on basis of the implementation for single finger thereby making use of a Guruswami–Sudan algorithm-based decoder for verification. The implementation, for which public C++ source code can be downloaded, is evaluated for single and various multi-finger settings using the MCYT-Fingerprint-100 database and provides security-enhancing features such as the possibility of combination with password and a slow-down mechanism.

Abstract: This is an undeniable fact that in the coming years a considerable percentage of organisations are drifting toward mobile devices for authentication. Banking sector as an additional offshoot has shifted to mobile devices with their applications for e-banking and mobile-banking, giving rise to an emergent requirement of a foolproof and authentic mobile-biometric system. This study presents an authentic mobile-biometric signature verification system and a comparative analysis of the performance of the proposed system for the two datasets; one using the standard device that is used for capturing biometric signatures and the other one is a mobile database taken from a smart phone for biometric signature authentication. The results presented demonstrate that the proposed system outperforms existing mobile-biometric signature verification systems based on dynamic time warping and hidden Markov model. Moreover, this study presents a comprehensive survey of mobile-biometric systems, different devices and hardware needed to support mobile biometrics along with open issues and challenges faced by the mobile-biometric systems. The experiments presented establish that the performance of mobile devices is low as compared with normal biometric signature capturing devices and the major reason the authors found is the absence of pen-tilt angle information in the mobile device datasets.

Abstract: In this study the authors will look at the detection and segmentation of the iris and its influence on the overall performance of the iris-biometric tool chain. The authors will examine whether the segmentation accuracy, based on conformance with a ground truth, can serve as a predictor for the overall performance of the iris-biometric tool chain. That is: If the segmentation accuracy is improved will this always improve the overall performance? Furthermore, the authors will systematically evaluate the influence of segmentation parameters, pupillary and limbic boundary and normalisation centre (based on Daugman's rubbersheet model), on the rest of the iris-biometric tool chain. The authors will investigate if accurately finding these parameters is important and how consistency, that is, extracting the same exact region of the iris during segmenting, influences the overall performance.

Abstract: Several cancellable biometrics (CBs) techniques have been proposed to protect biometric data and maintain users' privacy. Although such techniques can withstand brute-force and/or pre-image attacks, they are vulnerable to correlation attacks. In this study, the authors propose a novel correlation attack-resistant CBs scheme that is based on a convolution operation and a bidirectional associative memory (BAM) neural network. The proposed scheme utilises BAM to bind biometric templates to random bit-strings in a secure and efficient manner. These random bit-strings are then employed to derive cancellable templates from the true templates linked to them via BAM weights, which are safely stored with the generated cancellable template in the system database. In this study, linear convolution is adopted as the cancellable transformation process. The result of convolving the original biometric template with the transformation key is binarised according to a predefined threshold to thwart blind de-convolution. The security of the proposed scheme against different attacks is analysed and experiments on the CASIA-IrisV3-Interval dataset illustrate the efficacy of the proposed scheme.

Abstract: This study introduces a novel method to build up a multi-script off-line signature database aggregating many single-script off-line databases, along with a statistical performance analysis method for a fair comparison between single- and multi-script scenarios. This analysis method is based on merging the single-script databases without increasing the number of users (signers) and selecting the users for merging, based on the probability density functions of the users’ equal error rates. As similar results are achieved when merging single- and multi-script databases, it is concluded that multi-script signature verification is actually a generalisation and interoperability problem. The study also concludes that a statistical performance analysis method that is Bhattacharyya distance could be used for analysing multi-script versus single-script signature verification scenarios. These results have been obtained after experimenting with nine public databases with five different scripts.

Abstract: With the advent of modern technology, the use of biometric authentication systems has been on the rise. The core of any biometric system consists of a database which contains the biometric traits of the successfully enrolled users. As such, maintenance of the security of the database is paramount, i.e. it must be made sure that the contents of the database should not be compromised to foreign threats or adversaries. Biometric encryption (BE) is by far the most successfully studied and analysed technique used for providing this required level of security in biometric systems. In this survey, we discuss the intuition behind this idea and study in deep the key-binding-based mechanisms of BE which will provide a basic foundation for future novel researches in this area. In addition to the latest available survey, our paper investigates in details the core ideas behind the development of the fuzzy frameworks and includes the most recent works in the available literature. This study is concluded by inspecting the merging of multimodal biometrics with the fuzzy systems and discussing some open challenges in this domain.

Abstract: Although current face recognition systems in biometrics field are accurate enough to be used as substitutes for passwords or keys, most of them are prone to face spoofing attacks. Different techniques for face spoofing identification have been researched but most of them introduce additional sensors and are not cost or computationally efficient. In this study, the authors study the possibility of using individual differences in facial expressions for improving a face recognition system and make it immune to spoofing attacks. The authors develop a soft biometric neural-network-based system for video-based face recognition by analysing patterns in individual facial expressions on multiple frames. Results show that such a system is possible and has accuracies higher than 85%. Used alongside with a standard principal component analysis-based face recognition system, the combined method achieved 94.5% accuracy on Honda/UCSD Video Database and 92.9% on Youtube Faces DB, comparable with state-of-the-art. When tested against photo spoofing attacks on three public anti-spoofing databases the proposed method was immune. In terms of video spoofing, the error rate for the authors' proposed method was 1% surpassing state-of-the-art methods.

Abstract: This study introduces an approach for user authentication using free-text keystroke dynamics which incorporates text in Arabic language. The Arabic language has completely different characteristics to those of English. The approach followed in this study involves the use of the keyboard's key-layout. The method extracts timing features from specific key-pairs in the typed text. Decision trees were exploited to classify each of the users' data. In parallel for comparison, support vector machines were also used for classification in association with an ant colony optimisation feature selection technique. The results obtained from this study are encouraging as low false accept rates and false reject rates were achieved in the experimentation phase. This signifies that satisfactory overall system performance was achieved by using the typing attributes in the proposed approach, while typing Arabic text.

Abstract: Securing personal information on handheld devices, especially smartphones, has gained a significant interest in recent years. Yet, most of the popular biometric modalities require additional hardware. To overcome this difficulty, the authors propose utilising the existing visible light cameras in mobile devices. Leveraging visible vascular patterns on whites of the eye, they develop a method for biometric authentication suitable for smartphones. They start their process by imaging and segmenting whites of the eyes, followed by image quality assessment. The authors' stage 1 matcher is a three-step process that entails extracting interest points [Harris-Stephens, features from accelerated segment test, and speeded up robust features (SURF)], building features (SURF and fast retina keypoint) around those points, and match score generation using random sample consensus-based registration. Stage 2 matcher uses registered Gabor phase filtered images to generate orientation of local binary pattern features for its correlation-based match metric. A fusion of stage 1 and stage 2 match scores is calculated for the final decision. Using a dataset of 226 users, the authors' results show equal error rates as low as 0.04% for long-term verification tests. The success of their framework is further validated on UBIRIS v1 database.

Abstract: The handwritten signature is a biometric method used to verify a person's identity. This study lies within the scope of an online handwritten signature verification system, in which a signature is modelled by an analytical approach based on the empirical mode decomposition. The organised system is tested on the SVC2004 task1 and MYCT-100 databases. The implemented evaluation protocol shows the importance of the adopted method and allows obtaining an equal error rate of 1.83 and 2.23% for the SVC2004 task1 and the MYCT-100 databases, respectively.

Abstract: This study presents a new framework for human identity recognition using heart sound signals. The proposed framework is based on extracting cepstral features from heart sound signals, which are known as phono-cardio-gram (PCG). Two well-known cepstral features have been adopted in most of the previously implemented PCG biometric authentication systems; namely, mel-frequency and linear frequency cepstral features. In this study, two more cepstral features are proposed based on modifying the mel-frequency cepstral features. The first one is based on modifying the mel-frequency equation to increase the non-linearity of the triangular filters in the frequency range of the PCG signal. The other is based on replacing mel-scaled triangular filters with wavelet packet filters where a non-linear filter bank structure is designed using wavelet packet decomposition to select the appropriate bases for extracting discriminant features. The proposed system uses wavelet de-noising for pre-processing and linear discriminant analysis for classification. The proposed system is evaluated on two databases; one consists of 21 users (BioSec. database) and the other consists of 206 users (HSCT-11 database). Moreover, the proposed system is compared with previous systems that used the same databases. On the basis of the achieved results over the two databases, the two proposed cepstral features achieved higher correct recognition rates and lower error rates in identification and verification modes, respectively.

Abstract: Soft biometrics enable human description and identification from low-quality surveillance footage. This study premises the design, collection and analysis of a novel crowdsourced dataset of comparative soft biometric body annotations, obtained from a richly diverse set of human annotators. The authors annotate 100 subject images to provide a coherent, in-depth appraisal of the collected annotations and inferred relative labels. The dataset includes gender as a comparative trait and the authors find that comparative labels characteristically contain additional discriminative information over traditional categorical annotations. Using the authors' pragmatic dataset, semantic recognition is performed by inferring relative biometric signatures using a RankSVM algorithm. This demonstrates a practical scenario, reproducing responses from a video surveillance operator searching for an individual. The approach can reliably return the correct match in the top 7% of results with ten comparisons, or top 13% of results using just five sets of subject comparisons.

Abstract: To overcome the limitation of traditional illumination invariant methods for single sample face recognition, a modified version of gradientface named adaptively weighted orthogonal gradient binary pattern (AWOGBP), which is proved robust to illumination variation, is proposed in this study. First, the Tetrolet transform is performed on the images to obtain low frequency and high frequency components and the retina model processing is applied to low frequency component to make the image more robust to illumination, in the meantime, the authors multiply each element in high frequency components with a scale factor to accentuate details. Then the proposed AWOGBP is used to get the feature vectors of each direction and all the feature vectors are concatenated into the general feature vector for face recognition with the weights of the sub-graph based on their information entropy which is defined as the contribution to describe the whole face images. Finally the principle component analysis method is used to reduce dimensions and the nearest neighbour classifier is used for face image classification and recognition. Experimental results on CMU PIE and Extended Yale B face databases indicate that the proposed method is significantly better as compared with related state-of-the-art methods.

Abstract: Iris recognition systems typically enrol a person based on a single ‘best’ eye image. Research has shown that the probability of a false non-match result increases with increased difference in pupil dilation between the enrolment image and the probe image. Therefore, dilation-aware methods of enrolment should improve the accuracy of iris recognition. The authors examine a strategy to improve accuracy through a dilation-aware enrolment step that selects one or more enrolment images based on the observed distribution of dilation ratios for that eye. Additionally, they demonstrate that an image with median dilation is the optimal single eye image dilation-aware enrolment choice. Their results confirm that this dilation-aware enrolment strategy does improve matching accuracy compared with traditional single-image enrolment, and also compared with multi-image enrolment that does not take dilation into account.

Abstract: Using your face to unlock a mobile device is not only an appealing security solution, but also a desirable or entertaining feature that is comparable with taking selfies. It is convenient, fast, and does not require much effort. Nevertheless, for users with visual impairments, taking selfies could potentially be a challenging task. In order to study the usability and ensure the inclusion of mobile-based identity authentication technology, the authors have collected the blind-subject face database (BSFDB). Ensuring that technology is accessible to disabled people is important because they account for about 15% of the world population. The BSFDB contains some 40 individuals with visual disabilities who took selfies with a mock-up mobile device. The database comes with four experimental protocols which are defined by a dichotomy of two controlled covariates, namely, whether or not a subject is guided by audio feedback and whether or not he/she has received explicit instructions to take the selfie. The findings suggest the importance of appropriate design of human computer interaction as well as alternative feedback design based on the audio cue. All the data is available online including more than 70, 000 detected face images of blind and partially blind subjects.

Abstract: The authors explore the possibilities of a dense model-free three-dimensional (3D) face reconstruction method, based on image sequences from a single camera, to improve the current state of forensic face comparison. They propose a new model-free 3D reconstruction method for faces, based on the Lambertian reflectance model to estimate the albedo and to refine the 3D shape of the face. This method avoids any form of bias towards face models and is therefore suitable in a forensic face comparison process. The proposed method can reconstruct frontal albedo images, from multiple non-frontal images. Also a dense 3D shape model of the face is reconstructed, which can be used to generate faces under pose. In the authors’ experiments, the proposed method is able to improve the face recognition scores in more than 90% of the cases. Using the likelihood ratio framework, they show for the same experiment that for data initially unsuitable for forensic use, the reconstructions become meaningful in a forensic context in more than 60% of the cases.

Abstract: A combination of cryptographic and biometric systems, by performing specific binding technique on cryptographic key and biometric template, the fuzzy vault framework enhances the security level of current biometric cryptographic systems in terms of hiding secret key and protecting the template. Although the original scheme suggests the use of error-correction techniques (e.g. the Reed–Solomon code) to reconstruct the original polynomial, recent implementations do not share the same point of view. Instead, cyclic redundant code (CRC) is applied to identify the genuine polynomial from a set of candidates due to its simplicity. Within the scope of this study, the authors address a significant flaw of current CRC-based fuzzy vault schemes, which allows the potential of successful blend substitution attack. To overcome this problem, an integration of two novel modules into general fuzzy vault scheme, namely chaff point generator and verifier, is proposed. The new modules are designed to be integrated easily into the existing systems as well as simple to enhance. The proposed scheme can detect any modification in vault and, as a result, eliminate the blend substitution attack to improve general security. Moreover, the experimental results of this study with real-world datasets show an increase in genuine acceptance rates.

Abstract: In this study, the authors present two new techniques with their own particular advantages dedicated to the authentication of a person based on the three-dimensional geometry of the cornea. A device known as corneal topographer is used for capturing the shape of each cornea. Until now only a few studies on corneal biometry have been conducted and they were limited only to the anterior surface. In this study, since the whole cornea is a tissue layered by two (anterior and posterior) surfaces, the authors propose to use both surfaces to characterise the corneal shape. The first proposed method consists of comparing coefficients from a spherical harmonics decomposition, and this allows to do a fast comparison that can be used to perform many-to-one comparisons. The second approach is based on the minimal residual volume between two corneas after a registration step, this geometry-based method is more accurate but slower, and is thus used to perform one-to-one comparisons. A cascade fusion scheme is also proposed to benefit from the advantages of both methods. The authors’ study demonstrates that corneal shape could be used for biometry. The two proposed methods have been tested and validated on a dataset of 257 corneas.

Abstract: From the intuition that natural face images lie on or near a low-dimensional submanifold, the authors propose a novel spectral graph based dimensionality reduction method, named orthogonal enhanced linear discriminant analysis (OELDA), for face recognition. OELDA is based on enhanced LDA (ELDA), which takes into account both the discriminative structure and geometrical structure of the face space, and generates non-orthogonal basis vectors. However, a significant fact is that eliminating the dependence of basis vectors can promote more effective recognition of unseen face images. For this purpose, the authors seek to improve the ELDA scheme by imposing orthogonal constraints on the basis vectors. Experimental results on real-world face datasets show that, benefitting from orthogonality, OELDA has more locality preserving power and discriminative power than LDA and ELDA, and achieves the highest recognition rates among compared methods.

Abstract: Capturing age and sex of subjects from whole-body features has important applications in a wide variety of areas. However, current techniques for determining this information without subject interaction or high-resolution images are problematic. While computer vision techniques (e.g. poselets and histogram-oriented gradients) are functional at a stand-off, these methods often include areas influenced by characteristics such as clothing or hairstyle which vary by region and culture. Whole-body anthropometrics, especially those of children and youth experiencing rapid musculoskeletal changes, may help inform robust models of age estimation and sex classification. Models of anthropometric variables were developed from a pre-existing database for age estimation using linear regression techniques. Sex classification was performed both over the entire subject group as well as three individual age bins (2 ≤ subject age < 8, 8 ≤ subject age < 14, and 14 ≤ subject age). Age estimation models were highly dependent on head size and exhibited r-squared values as high as 0.91 and root mean square error values as low as 1.29 years. Sex classification was found to be highly linked to a combination of foot, hand, hip, and torso metrics for correct classification as high as 88%. The results presented herein may help develop and focus methods of determining age and sex.

Abstract: Biometrics involves the use of physical and/or behavioural traits for human authentication and identification. It is a well-established and growing discipline supporting many practical applications including cyber security and mobile devices. In recent years, stimulated by hardware advances and a rapidly growing consumer market built around increasingly powerful mobile phones and other portable platforms, mobile biometrics has become an important challenge for biometric applications and will continue to do so. It is predicted that the global market for mobile biometrics will grow substantially in the coming years. Mobile biometrics aims to achieve the functionality and robustness of conventional biometrics while also supporting portability and mobility in order to bring greater convenience, flexibility, and opportunity for its deployment in a wide range of operational environments ranging from consumer applications to law enforcement. However, achieving these aims brings new challenges, such as issues about power consumption, algorithmic complexity, device memory limitations, frequent changes in operational environment, security, durability, reliability, connectivity, and so on. This Special Issue (SI) aims to bring together some good examples of current research that addresses some of the major challenges for mobile biometrics. The SI provides a platform for both academic researchers and industry partners to become familiar with the latest new research, stimulating discussion on existing and emerging challenges in mobile biometrics, and raising awareness of potential solutions to advance research in mobile biometrics. After careful review and selection, this SI has accepted four papers from those submitted. These four papers cover mobile biometrics from different aspects, which is detailed in the following. The first paper, ‘A Method for Using Visible Ocular Vasculature for Mobile Biometrics’, by V. Gottemukkula, S. Saripalle, S. Tankasala, and R. Derakhshani, investigates the use of existing visible light cameras in mobile phones for vascular pattern segmentation, extraction and recognition. Several stages are involved in the biometric process, including interest point detection, feature description, and matching, with Gabor phase filters used for feature extraction and matching. Low error rates are reported in the experimental validation of the approach. The study reported shows the feasibility and utility of extracting vascular patterns for user recognition in mobile devices. The second paper, ‘An Authentic Mobile-Biometric Signature Verification System’, by F. Zareen and S. Jabin, presents an overview of typical mobile biometric systems, including different devices and hardware to support mobile biometrics. The paper discusses open issues and challenges for developing mobile biometric systems. To that end the paper describes a mobile biometric system for signature verification, which is supported by experimental validation. The third paper, ‘The Blind Subjects Face Database (BSFD)’, by N. Poh, R. Blanco-Gonzalo, R. Wong and R. Sanchez-Reillo, introduces a new face database captured from blind subjects. The underlying application scenario is to use the face characteristic to unlock a mobile device, which is convenient and fast for real applications. However, one needs to address how well this technique can be used for visually impaired people. The collected database, BSFD, facilitates the usability and face recognition studies involving people with varying degrees of visual impairment for mobile applications. More importantly, the database is publicly available for use by other researchers to investigate related issues further. The last paper, ‘Small Fingerprint Scanners Used in Mobile Devices, The Impact on Biometric Performance’, by B. Fernandez-Saavedra, R. Sanchez-Reillo, R. Ros-Gomez, and J. Liu-Jimenez, examines the performance of fingerprint recognition in mobile devices where the embedded fingerprint scanners could have different sensing areas, capturing fingerprint images of different image quality. Both public and commercial algorithms (including a database specifically assembled for this study) are used for the testing and performance evaluation. Experimental results show that the fingerprint matching performance is affected by the scanner sizes, which are correlated to the fingerprint image quality.

Abstract: Fingerprint quality assessment (FQA) has been a challenging issue due to a variety of noisy information contained in the samples, such as physical defect and distortions caused by sensing devices. Existing studies have made efforts to find out more suitable techniques for assessing fingerprint quality but it is difficult to achieve a common solution because of, for example, different image settings. This study is two-fold, related to FQA, including a literature review of the prior work in assessing fingerprint image quality and the associated evaluation approaches. First, the authors categorised some representative studies proposed in last few decades to show how this problem has been solved so far. Second, this study gives a brief introduction of the associated evaluation approaches, and then contributes an extended evaluation framework based on the enrolment selection, which offers repeatable and statistically convincing measures for evaluating quality metrics. Experimental results demonstrate the usability of the proposed evaluation framework via offline trials.

Abstract: An ear recognition system represents a powerful tool in forensic applications. Even in case the facial characteristic of a suspect is partly or fully covered an image of the outer ear may suffice to reveal a subject's identity. In forensic scenarios imagery may stem from surveillance cameras of environments where image compression is common practice to overcome limitations of storage or transmission capacities. Yet, the impact of severe image compression on ear recognition has remained undocumented. In this work the authors analyse the influence of different state-of-the-art image compression standards on ear detection and ear recognition algorithms. Evaluations conducted on an uncompressed ear database are considered with respect to different stages in the processing chain of an ear recognition system where compression may be applied, representing the most relevant forensic scenarios. Experimental results are discussed in detail highlighting the potential and limitations of automated ear recognition in presence of image compression.

Abstract: Biometrics has burst into mobile technology. Fingerprint scanners are being embedded in smartphones and tablets supplying these devices with the security and usability provided by biometric authentication mechanisms. However, performance results obtained by biometric systems cannot be extrapolated to mobile devices. The conditions change, especially at capture process, due to the reduced sensing area of the scanners used. The impact of small fingerprint scanners on the quality and biometric performance of the system is studied. A database using three different fingerprint scanners has been collected and reduced-size images (i.e. 12 × 12 mm 2 , 10 × 10 mm 2 and 8 × 8 mm 2 ) have been modelled by cropping the original ones. Performance testing has been conducted using one public and one commercial algorithm, and considering two application scenarios. One scenario in which enrolment and authentication are executed using the same small sensor included in the mobile device (i.e. cropped image against cropped image) and a second scenario in which enrolment is executed using an external larger sensor and authentication is done using the mobile device sensor (i.e. full image against cropped image). Results show the gradual worsening of quality and error rates as the size of the fingerprint scanner is reduced revealing a significant difference between the application scenarios analysed.

Abstract: The face and gesture recognition network (FG-NET) ageing database was released in 2004 in an attempt to support research activities aimed at understanding the changes in facial appearance caused by ageing. Since then the database was used for carrying out research in various disciplines including age estimation, age-invariant face recognition and age progression. On the basis of the analysis of published work where the FG-NET ageing database was used, conclusions related to the type of research carried out in relation to the impact of the dataset in shaping up the research topic of facial ageing are presented. This study also includes a review of key articles from different thematic areas, where the FG-NET ageing database was used and the presentation of benchmark results. The ultimate aims of this study are to present concrete facts related to research activities in facial ageing during the past decade, provide an indication of the main methodologies adopted, present a comprehensive list of benchmark results and most importantly provide roadmaps for future trends, requirements and research directions in facial ageing.