A peripheral market?

Considering the number of PCs that could benefit from the addition of biometric user identification, the market for biometric PC peripheral devices is potentially massive. But, to date, the number of sales is disappointing, and in some cases is falling. There are many potential drivers and detractors at play, so it may take another few years before the overall dynamics of this market become easier to predict.     

Ear,eye, gait,keystroke, lip and nailbed biometrics

Inventive biometric development continues into the new millennium with researchers striving to bring lesser known biometric technologies to the fore. Btt predicts this to be the year of keystroke dynamics, as the commercial viability of the technology gets tantalisingly close. Gait recognition wins the award for generating the most media interest, but is still some way from becoming mainstream.     

Recognition versus identity — the role of biometrics

The distinction between the recognition of an individual using an automated biometric system and the fundamental notion of their identity is subtle and therefore provokes much debate relating to privacy and other societal issues. This article seeks to clarify some of these issues relating to biometric recognition and identity by providing a framework, and by distinguishing between the technology and societal issues.     

Voter Integrity,Trust and the Promise of Digital Technologies: Biometric Voter Registration in Solomon Islands

Drawing on the anthropology of technology, this article examines the introduction of a digital biometric voter registration for Solomon Islands 2014 national election. Four perspectives on biometric voting are brought into dialogue: (1) the technological particularities, strengths and shortcomings of biometric voting registration (BVR), (2) a global and international embrace of the technology for its perceived ‘universal’ tendency to secure identities, (3) efforts by the Solomon Islands state to showcase its political stability by means of BVR and (4) the ways village-based voters come to understand, interpret and re-imagine BVR as political technology. We show how, within the ethnographic context of North Malaita, debates surrounding BVR reveal a continued distrust and uncertainty in North Malaitans’ relationship with the Solomon Islands state and its representatives. Within the context of this uncertainty BVR is re-imagined as technology that aids voter integrity within rather than beyond patronage networks.     

High-security photoacoustic identity recognition by capturing hierarchical vascular structure of finger

Currently, most biometric methods mainly use single features, making them easily forged and cracked. In this study, a novel triple-layers biometric recognition method, based on photoacoustic microscopy, is proposed to improve the security of biometric identity recognition. Using the photoacoustic (PA) dermoscope, three-dimensional absorption-structure information of the fingers was obtained. Then, by combining U-Net, Gabor filtering, wavelet analysis and morphological transform, a lightweight algorithm called photoacoustic depth feature recognition algorithm (PADFR) was developed to automatically realize stratification (the fingerprint, blood vessel fingerprint and venous vascular), extracting feature points and identity recognition. The experimental results show that PADFR can automatically recognize the PA hierarchical features with an average accuracy equal to 92.99%. The proposed method is expected to be widely used in biometric identification system due to its high security.     

APIs and Interoperability

The take-up of biometric solutions continues to accelerate, especially in the emerging market of intranet and internet applications. This surge in demand is partly due to improved technology, but another crucial factor has been the significant progress made on standards and interoperability issues     

A Multifaceted Independent Performance Analysis of Facial Subspace Recognition Algorithms

Face recognition has emerged as the fastest growing biometric technology and has expanded a lot in the last few years. Many new algorithms and commercial systems have been proposed and developed. Most of them use Principal Component Analysis (PCA) as a base for their techniques. Different and even conflicting results have been reported by researchers comparing these algorithms. The purpose of this study is to have an independent comparative analysis considering both performance and computational complexity of six appearance based face recognition algorithms namely PCA, 2DPCA, A2DPCA, (2D)²PCA, LPP and 2DLPP under equal working conditions. This study was motivated due to the lack of unbiased comprehensive comparative analysis of some recent subspace methods with diverse distance metric combinations. For comparison with other studies, FERET, ORL and YALE databases have been used with evaluation criteria as of FERET evaluations which closely simulate real life scenarios. A comparison of results with previous studies is performed and anomalies are reported. An important contribution of this study is that it presents the suitable performance conditions for each of the algorithms under consideration.     

Personal identification by eyes

Identification of persons through the eyes is in the field of biometrical science. Many security systems are based on biometric methods of personal identification, to determine whether a person is presenting itself truly. The human eye contains an extremely large number of individual characteristics that make it particularly suitable for the process of identifying a person. Today, the eye is considered to be one of the most reliable body parts for human identification. Systems using iris recognition are among the most secure biometric systems.     

A bill of health for biometrics?

Few industries have such a need to promote privacy, fight fraud and cut costs at so many different levels as the healthcare market. Yet despite readily available biometric solutions on offer to combat these issues, Btt reveals this potentially lucrative sector is not yet yielding returns.     

Applications of deep convolutional neural networks to predict length,circumference, and weight from mostly dewatered images of fish

Simple biometric data of fish aid fishery management tasks such as monitoring the structure of fish populations and regulating recreational harvest. While these data are foundational to fishery research and management, the collection of length and weight data through physical handling of the fish is challenging as it is time consuming for personnel and can be stressful for the fish. Recent advances in imaging technology and machine learning now offer alternatives for capturing biometric data. To investigate the potential of deep convolutional neural networks to predict biometric data, several regressors were trained and evaluated on data stemming from the FishL? Recognition System and manual measurements of length, girth, and weight. The dataset consisted of 694 fish from 22 different species common to Laurentian Great Lakes. Even with such a diverse dataset and variety of presentations by the fish, the regressors proved to be robust and achieved competitive mean percent errors in the range of 5.5 to 7.6% for length and girth on an evaluation dataset. Potential applications of this work could increase the efficiency and accuracy of routine survey work by fishery professionals and provide a means for longer‐term automated collection of fish biometric data.     

Taking the pulse of nations: A biometric measure of well-being

A growing literature identifies associations between subjective and biometric indicators of wellbeing. These associations, together with the ability of subjective wellbeing metrics to predict health and behavioral outcomes, have spawned increasing interest in wellbeing as an important concept in its own right. However, some social scientists continue to question the usefulness of wellbeing metrics. We contribute to this literature in three ways. First, we introduce a biometric measure of wellbeing – pulse – that hs been little used. Using nationally representative data on 165,000 individuals from the Health Survey for England and Scottish Health Surveys we show that its correlates are similar in a number of ways to those for happiness, and that it is highly correlated with wellbeing metrics, as well as self-assessed health. Second, we examine the determinants of pulse rates in mid-life (age 42) among the 9000 members of the National Child Development Study, a birth cohort born in a single week in 1958 in Britain. Third, we track the impact of pulse measured in mid-life (age 42) on health and labor market outcomes at age 50 in 2008 and age 55 in 2013. The probability of working at age 55 is negatively impacted by pulse rate a decade earlier. The pulse rate has an impact over and above chronic pain measured at age 42. General health at 55 is lower the higher the pulse rate at age 42, while those with higher pulse rates at 42 also express lower life satisfaction and more pessimism about the future at age 50. Taken together, these results suggest social scientists can learn a great deal by adding pulse rates to the metrics they use when evaluating people’s wellbeing.     

Biometrics and smart cards

Smart card chips are increasingly being used to store and match biometric templates. In the near future, however, the security bar will be raised even further as fingerprint sensors are placed on the smart cards themselves. At this point the combined market for smart cards and biometrics could really blossom.     

Forging ahead

A large percentage of the world’s population have seen science fiction movies depicting biometric systems that can be circumvented by criminals using sophisticated devices. Such misleading information may prove to be a box office winner, but it can lead to a high amount of confusion about how a biometric device assesses whether a person is who he or she claims to be. While the marketing departments of many biometrics companies address the PR issues associated with inaccurate information, the technical divisions are also working hard to eliminate problems associated with ‘live and well’ checks. However, to my knowledge, in spite of the steps taken by many fingerprint companies, no fingerprint system — currently on the market — is 100% foolproof.     

Locality Constrained Joint Dynamic Sparse Representation for Local Matching Based Face Recognition

Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.     

Does Iris Change Over Time?

Iris as a biometric identifier is assumed to be stable over a period of time. However, some researchers have observed that for long time lapse, the genuine match score distribution shifts towards the impostor score distribution and the performance of iris recognition reduces. The main purpose of this study is to determine if the shift in genuine scores can be attributed to aging or not. The experiments are performed on the two publicly available iris aging databases namely, ND-Iris-Template-Aging-2008–2010 and ND-TimeLapseIris-2012 using a commercial matcher, VeriEye. While existing results are correct about increase in false rejection over time, we observe that it is primarily due to the presence of other covariates such as blur, noise, occlusion, and pupil dilation. This claim is substantiated with quality score comparison of the gallery and probe pairs.     

Two-stage biometric authentication method using thought activity brain waves

Brain waves are proposed as a biometric for verification of the identities of individuals in a small group. The approach is based on a novel two-stage biometric authentication method that minimizes both false accept error (FAE) and false reject error (FRE). These brain waves (or electroencephalogram (EEG) signals) are recorded while the user performs either one or several thought activities. As different individuals have different thought processes, this idea would be appropriate for individual authentication. In this study, autoregressive coefficients, channel spectral powers, inter-hemispheric channel spectral power differences, inter-hemispheric channel linear complexity and non-linear complexity (approximate entropy) values were used as EEG features by the two-stage authentication method with a modified four fold cross validation procedure. The results indicated that perfect accuracy was obtained, i.e. the FRE and FAE were both zero when the proposed method was tested on five subjects using certain thought activities. This initial study has shown that the combination of the two-stage authentication method with EEG features from thought activities has good potential as a biometric as it is highly resistant to fraud. However, this is only a pilot type of study and further extensive research with more subjects would be necessary to establish the suitability of the proposed method for biometric applications.     

Using Head Patch Pattern as a Reliable Biometric Character for Noninvasive Individual Recognition of an Endangered Pitviper Protobothrops mangshanensis

Mangshan pitviper, Protobothrops mangshanensis(formerly Zhaoermia mangshanensis) is endemic to China. Unfortunately, due to the decreasing size of its wild populations, this snake has been listed as critically endangered. Research carried out on the Mangshan pitviper's population ecology and captive reproduction has revealed that the unique head patch patterns of different individuals may potentially be used as a noninvasive recognition biometric character. We collected head patch pattern images of 40 individuals of P. mangshanensis between 1994 and 2011. By comparing each pitviper's head patch pattern, we found that the head patch pattern of individual snakes was different and unique. Additionally, we observed and recorded the head patch pattern characters of four adults and five juveniles before and after ecdysis. Our findings confirmed that head patch patterns of Mangshan pitvipers are unique and stable, remaining unchanged after ecdysis. Thus, individuals can be quickly identified by examining the head patch pattern within a specific recognition area on the head. This method may be useful for noninvasive individual recognition in many other species that display color patch pattern variations, especially in studies of endangered species where the use of invasive marking techniques is undesirable.     

Human mouse trap

A new biometric that tracks the unique movements created when a user signs their name using a regular computer mouse could be an excellent and cheap addition to improve computer security. Although at an early stage of development, the technology has already received significant interest and with further work could be a commercially viable product.     

Securing web services with biometrics

Web services are evolving and gaining wide acceptance as a standard for distributed computing. Organisations are starting to rely on them to conduct their core business, thereby enabling access to a huge amount of sensitive personal, medical and financial information that they hold, as well as information held on behalf of their customers, such as governments. Clearly, security for such web services is critical. Biometrics can play an important role in web services in a number of ways, providing improved levels of security and convenience for end-users. Emerging XML-based standards for carrying biometric information and exploiting cryptographic techniques may act as an enabler for biometrics.There has been considerable excitement in the world of information technology about web services. Indeed, they are seen as the next major revolution hitting the internet, enabling applications and organisations to interact with one another more directly and efficiently. However, security for web services is seen as a major challenge to be overcome before widespread deployment is possible. At the same time, biometric technologies continue to advance, with growing impetus behind the establishment of common standards for distributed biometric systems. This article explores web services and biometrics and examines the potential for the technologies to be of mutual benefit to each other.     

Biometric sex discrimination is unreliable when sexual dimorphism varies within and between years: an example in Eurasian Oystercatchers Haematopus ostralegus

Molecular sexing of birds has been possible for over a decade, but for practical reasons many studies still use biometric data for sex discrimination. In some species, the sexes are easy to distinguish but sexual dimorphism is often more subtle, requiring the use of statistical analyses of biometric measurements to discriminate sexes. These models are usually parameterized and validated using data from a limited number of sites and years. However, the resulting discriminant functions are often applied to other populations and periods. A crucial, but usually untested, assumption is that sexual dimorphism does not vary in time and space. Here we illustrate the consequences of violation of this assumption in Eurasian Oystercatchers Haematopus ostralegus , a species for which most studies have relied on biometric sexing. Using biometric data from captures of known-sex birds, we show that sexual dimorphism varied substantially in time and even reversed in some months and years. Furthermore, some biometric traits used in sexing changed gradually over time, causing a reduction in sexual dimorphism. We show that the consequences of this variation on sex discrimination in Oystercatchers are subtle and easily overlooked, but can result in inaccurate and strongly male- or female-biased sex-ratio estimates. We recommend that biometric sexing should be avoided in Oystercatchers unless specific calibration for each month, year and area is carried out. This recommendation also applies to other species where biometric traits may depend on environmental conditions. We argue that this condition might apply to many bird species and therefore advise caution when interpreting results based on biometric sex discrimination.