EU Data Protection Legislation and Case-Law with Regard to Biometric Applications

Iglezakis, Ioannis

doi:10.2139/ssrn.2281108

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…However, although most aspects can be accurately evaluated in ABC systems, the accuracy evaluation faces some challenges because too few impostor attempts are present [MacLeod and McLindin 2011] and collected biometric data are not readily made public for privacy reasons. In fact, the legislation of some countries does not allow the public dissemination of biometric data (particularly regarding the fingerprint and iris traits), match scores, and decision thresholds obtained and used by the biometric systems installed for government activities [Sprokkereef 2008;Iglezakis 2013]. Therefore, it is not possible to compute the typical figures of merit used in the accuracy evaluation of biometric systems, such as the FAR, FRR, and receiver operating characteristic (ROC) [Gamassi et al 2005a;Mansfield and Wayman 2002], and the evaluation of the accuracy primarily relies on figures of merit computed using technology evaluations performed on public datasets or using internal testing procedures [MacLeod and McLindin 2011].…”

Section: Evaluation Of the Biometric Recognitionmentioning

confidence: 99%

Biometric Recognition in Automated Border Control

et al. 2016

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The increasing demand for traveler clearance at international border crossing points (BCPs) has motivated research for finding more efficient solutions. Automated border control (ABC) is emerging as a solution to enhance the convenience of travelers, the throughput of BCPs, and national security. This is the first comprehensive survey on the biometric techniques and systems that enable automatic identity verification in ABC. We survey the biometric literature relevant to identity verification and summarize the best practices and biometric techniques applicable to ABC, relying on a real experience collected in the field. Furthermore, we select some of the major biometric issues raised and highlight the open research areas.

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Section: Evaluation Of the Biometric Recognitionmentioning

confidence: 99%

Biometric Recognition in Automated Border Control

et al. 2016

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“…Privacy protection represents another important challenge for the development of viable fusion methods for ABC systems. In some countries, the legal framework denies the possibility to store and disseminate biometric data obtained from government systems [6], resulting in a design problem from two perspectives. First, many advanced fusion techniques, such as classifier-based techniques [4], require a preliminary training to tune some of the parameters.…”

Section: Multimodal Biometric Fusion In Abc Systems a Challengesmentioning

confidence: 99%

“…It is therefore necessary to rely on evaluations carried out using public datasets or with internal testing procedures [7]. An alternative is to perform the analysis using a subset of the transactions carried out by the ABC system that guarantees privacy protection [6].…”

Section: Multimodal Biometric Fusion In Abc Systems a Challengesmentioning

confidence: 99%

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Enhancing the Performance of Multimodal Automated Border Control Systems

Anand

Labati

Genovese

et al. 2016

2016 International Conference of the Biometrics Special Interest Group (BIOSIG)

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Biometric recognition in Automated Border Control (ABC) systems is performed in response to an increased worldwide traffic, by automatically verifying the identity of the passenger during border crossing. Currently, ABC systems seldom use methods for multimodal biometric fusion, which have been proved to increase the recognition accuracy, due to technological and privacy limitations. This paper proposes a framework for the biometric fusion in ABC systems, with the features of being technology-neutral and privacy-compliant, by performing an analysis of the most suitable biometric fusion techniques for ABC systems and considering the current technical and legal limitations.

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“…However, the regulations of some countries pose strong restrictions on the use of biometric data captured for government applications (e.g., border control) [27], limiting the applicability of score normalization techniques. These regulations regard the type of data stored in ABC systems, limit the amount of usable information, and impose the use of well-known cryptographic algorithms (e.g., AES) that differ from template protection methods specifically designed for biometric systems [28]- [30].…”

Section: Introductionmentioning

confidence: 99%

Enhancing fingerprint biometrics in Automated Border Control with adaptive cohorts

Anand

Labati

Genovese

et al. 2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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Automated Border Control (ABC) systems are being increasingly used to perform a fast, accurate, and reliable verification of the travelers' identity. These systems use biometric technologies to verify the identity of the person crossing the border. In this context, fingerprint verification systems are widely adopted due to their high accuracy and user acceptance. Matching score normalization methods can improve the performance of fingerprint recognition in ABC systems and mitigate the effect of non-idealities typical of this scenario without modifying the existing biometric technologies. However, privacy protection regulations restrict the use of biometric data captured in ABC systems and can compromise the applicability of these techniques. Cohort score normalization methods based only on impostor scores provide a suitable solution, due to their limited use of sensible data and to their promising performance. In this paper, we propose a privacy-compliant and adaptive normalization approach for enhancing fingerprint recognition in ABC systems. The proposed approach computes cohort scores from an external public dataset and uses computational intelligence to learn and improve the matching score distribution. The use of a public dataset permits to apply cohort normalization strategies in contexts in which privacy protection regulations restrict the storage of biometric data. We performed a technological and a scenario evaluation using a commercial matcher currently adopted in real ABC systems and we used data simulating different conditions typical of ABC systems, obtaining encouraging results.

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EU Data Protection Legislation and Case-Law with Regard to Biometric Applications

Cited by 3 publications

References 8 publications

Biometric Recognition in Automated Border Control

Biometric Recognition in Automated Border Control

Enhancing the Performance of Multimodal Automated Border Control Systems

Enhancing fingerprint biometrics in Automated Border Control with adaptive cohorts

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