Biometrics And Identity

A recent trend in migration policy in Europe is the increased use of Information and Communication Technologies (ICTs) for border control purposes. A growing academic literature explains the digitisation of border controls as an instance of the post-9/11 securitisation of migration policy. This paper re-examines why European states are digitising their border controls, and then explores how ‘pre-emptive mobility governance’ works. Although security imperatives play a role in accelerating digitisation, a securitisation framing obscures continuities with pre-9/11 practices and underplays other policy drivers. Pre-emptive mobility governance is best characterised as a digital-era version of ‘remote control’, and is shaped by other organisational and political rationales—first, instrumental beliefs about the efficiency gains of border technologies; second, their symbolic role in the context of the domestic politicisation of immigration. The paper then considers how ICTs are reshaping the tools of mobility governance, enabling three distinct modes of pre-emptive detection and effect.

Using the example of Eurodac the following contribution concentrates on the digitization of European border controls. Eurodac, an information, communication and control technology, operates by means of a European database, in which the fingerprints of asylum seekers and irregular migrants are stored. Eurodac works as a so-called Automated Fingerprint Identification System (AFIS) and is applied within the areas where the rules of the Dublin III regulation are applicable. Dublin/ Eurodac were designed in relation to each other and in response to the crisis of the European asylum system, which was accompanied by the construction and the use of rather lax and crude terms such as "asylum shopping." 1 The Dublin III regulation is based on the "polluter-pays" principle. It states that the Member State which has "caused" the entry of an asylum applicant (for instance by granting a visa or for lack of border security) should perform the asylum procedure. By using the Eurodac database to reconstruct the one and only responsible Member State per

Purpose: This study aims to explore whether face recognition technology - as it is intensely employed by state and local police departments and law enforcement agencies - is racism-free or, on the contrary, is affected by racial biases and/or racist prejudices, thus reinforcing overall racial discrimination.

Design/Methodology/Approach: The study investigates the causal pathways through which face recognition technology may reinforce the racial disproportion in enforcement; it also inquires whether it further discriminates black people by making them experience more racial discrimination, and self-identify more decisively as black - two conditions that are shown to be harmful in various respects.

Findings: This study shows that face recognition technology, as it is produced, implemented and used in Western societies, reinforces existing racial disparities in stop, investigation, arrest and incarceration rates due to racist prejudices, and even contributes to strengthen the unhealthy effects of racism on historically disadvantaged racial groups, like black people.

Practical implications: The findings hope to make law enforcement agencies and software companies aware that they must take adequate action against the racially discriminative effects of the use of face recognition technology.

Social implications: This paper highlights that no implementation of an allegedly racism-free biometric technology is safe from the risk of racially discriminating, simply because each implementation leans against our society, which is affected by racism in many persisting ways.

Originality/Value: While the ethical survey of biometric technologies is traditionally framed in the discourse of universal rights, this study explores an issue that has not been deeply scrutinised so far, that is, how face recognition technology differently affects distinct racial groups, and how it contributes to racial discrimination.

In this research, we investigated the performance of a continuous biometric authentication system for mobile devices under various different analysis techniques. We tested these on a publicly available swipe gestures database with 71 users, but the techniques can also be applied to other biometric modalities in a continuous setting. The best result obtained in this research is that (1) none of the 71 genuine users is lockout from the system; (2) for 68 users we require on average 4 swipe gestures to detect an imposter; (3) for the remaining 3 genuine users, on average 14 swipes are required while 4 impostors are not detected.

This paper is set out to evaluate the performance of feature extraction techniques that can determine ethnicity of an individual using fingerprint biometric technique and deep learning approach. Hence, fingerprint images of one thousand and fifty-four (1054) persons of three different ethnic groups (Yoruba, Igbo and Middle-Belt) in Nigeria were captured. Kernel Principal Component Analysis (K-PCA) and Kernel Linear Discriminant Analysis (KLDA) were used independently for feature extraction while Convolutional Neural Network (CNN) was used for supervised learning of the features and classification. The results showed that out of sixty (60) individual fingerprints tested, eight (8) were classified as Yoruba, forty-eight (48) as Igbo and four (4) as Hausa. The Recognition Accuracy for K-PCA was 93.97% and KLDA was 97.26%. For Average Recognition time, K-PCA used 9.98seconds while KLDA used 10.02seconds. The memory space utilized by K-PCA was 94.57KB while KLDA utilized Original Research Article Aworinde et al.; AJRCOS, 3(1): 1-9, 2019; Article no.AJRCOS.47947 2 52.17KB. T-Test paired sample statistics was carried out on the result obtained; the outcome presented reveal that KLDA outperformed the K-PCA technique in terms of Recognition Accuracy. The relationship between the average recognition time () and threshold value (ℎ) was found to be polynomial of order four (4) with a high correlation coefficient for KPCA and polynomial of order three (3) with a high correlation coefficient for KLDA. In terms of computation time analysis, KLDA is computationally more expensive than KPCA by reason of processing speed.

This paper discusses the methods of presentation and comparison of semantically unrelated
images with an assessment of their similarity in original feature space, and in the Space of Canonical Variables
(SCV). The projection of source images in SCV is implemented using a twodimensional canonical correla
tion analysis algorithm (2D CCA/2D KLT).

Optical coherence tomography (OCT) is a high-resolution imaging technology capable of capturing a
three-dimensional (3-D) representation of fingertip skin. The papillary junction—a junction layer of skin containing
the same topographical features as the surface fingerprint—is contained within this representation. The top
edge of the papillary junction contains the topographical information pertinent to the internal fingerprint.
Extracting the internal fingerprint from OCT fingertip scans has been shown to be possible. Currently, acquiring
the internal fingerprint involves manually defining the region containing it. This manner of definition is inefficient.
Perfect knowledge of the location of the papillary junction is hypothesized as achievable. This research details
and tests a k-means clustering approach for papillary junction detection. All tested metrics are of a standard
comparable to the measured human error. The technique presented in this research is highly successful in
detection of the location of the papillary junction. Furthermore, high-quality internal fingerprints are acquired
using the coordinates obtained.

Continuous Authentication by analysing the user's behaviour profile on the computer input devices is challenging due to limited information, variability of data and the sparse nature of the information. As a result, most of the previous research was done as a periodic authentication, where the analysis was made based on a fixed number of actions or fixed time period. Also, the experimental data was obtained for most of the previous research in a very controlled condition, where the task and environment were fixed. In this paper, we will focus on actual continuous authentication that reacts on every single action performed by the user. The experimental data was collected in a complete uncontrolled condition from 52 users by using our data collection software. In our analysis, we have considered both keystroke and mouse usages behaviour pattern to avoid a situation where an attacker avoids detection by restricting to one input device because the continuous authentication system only checks the other input device. The result we have obtained from this research is satisfactory enough for further investigation on this domain.