Large-scale Biometric Systems Research Articles

Big Data approach to biometric-based identity analytics

Very large-scale biometric systems are becoming mainstream in nationwide identity cards and mobile secure payment methods. As with other Big Data systems, biometric systems contend with the “four V” challenges that involve the effective managing of the complex life cycle and operations of identity information—despite the immense enrollment database size ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">volume</i> ) and rapid transaction response-time ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">velocity </i> ) requirements using potentially noisy, fraudulent ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">veracity</i> ), and multiple ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">variety </i> ) biometric identifiers. We describe representative techniques addressing these challenges in a variety of diverse and realistic biometrics applications such as 1) indexing methods that allow access in near constant time (velocity) irrespective of the scale (volume) of the biometric database, 2) fusing multiple (variety) biometrics to address risk and accuracy concerns, and 3) ensuring the integrity (veracity) of biometric databases by eliminating multiple duplicate records as well as protecting against theft of biometric identifiers. While sharing many commonalities with generic Big Data system-design issues, biometric systems also provide a rich case study involving how these issues manifest and are addressed in a unique, domain-specific way. We believe that by virtue of dealing with some of the most critical entities, namely identity and entitlement, biometric systems are likely to emerge as among the most critical of the Big Data systems.

Legally compatible design of future biometric systems for crime prevention

Innovations in emerging technologies have an impact on privacy and fundamental rights. For example, emerging technologies include future biometric systems for crime prevention. In contrast to large-scale biometric systems for migration control, these systems promise to track down terrorists and organized criminals but also entail novel risks for society. One can govern science and technology by means of privacy impact assessments (which are currently being researched). Alternatively there is the approach – used in this paper – that primarily builds on the assumption that technology design can render the violation of a legal norm impossible. Using German and European constitutional law as an example, this paper will present proposals for technology design derived from the law.

Service-oriented architecture based on biometric using random features and incremental neural networks

We propose a service-oriented architecture based on biometric system where training and classification tasks are used by millions of users via internet connection. Such a large-scale biometric system needs to consider template protection, accuracy and efficiency issues. This is a challenging problem since there are tradeoffs among these three issues. In order to simultaneously handle these issues, we extract both global and local features via controlling the sparsity of random bases without training. Subsequently, the extracted features are fused with a sequential classifier. In the proposed system, the random basis features are not stored for security reason. The non-training based on feature extraction followed by a sequential learning contributes to computational efficiency. The overall accuracy is consequently improved via an ensemble of classifiers. We evaluate the performance of the proposed system using equal error rate under a stolen-token scenario. Our experimental results show that the proposed method is robust over severe local deformation with efficient computation for simultaneous transactions. Although we focus on face biometrics in this paper, the proposed method is generic and can be applied to other biometric traits.

Human Factors in Large-Scale Biometric Systems: A Study of the Human Factors Related to Errors in Semiautomatic Fingerprint Biometrics

The purpose of this paper is to demonstrate the importance of considering human factors in large-scale, complex biometric systems. A team of 19 board-certified latent print examiners conducted 1620 latent fingerprint image formatting tasks, 1797 encoding tasks, and 146 388 side-by-side comparison tasks of latent prints with potential matching tenprint candidates. Examiner feedback from ten encoding mistakes and 13 comparison mistakes provided significant data to demonstrate the deserved consideration of relating the Department of Defense (DoD) Human Factors Classification System (HFACS) to semiautomatic fingerprint biometrics. The increase in match rate of 10% and 7%, respectively, for encoding and comparison when verifications are conducted on these tasks provides striking evidence of the risks involved if large-scale biometric system integrators or owners design and operate biometric systems based solely on single human examiner conclusions without ample consideration of the error recovery mechanism of second-examiner involvement for low-quality data.

Biometric classifier update using online learning: A case study in near infrared face verification

The performance of a large scale biometric system may deteriorate over time as new individuals are continually enrolled. To maintain an acceptable level of performance, the classifier has to be re-trained offline in batch mode using both existing and new data. The process of re-training can be computationally expensive and time consuming. This paper presents a new biometric classifier update algorithm that incrementally re-trains the classifier using online learning and progressively establishes a decision hyperplane for improved classification. The proposed algorithm incorporates soft labels and granular computing in the formulation of a 2 ν -Online Granular Soft Support Vector Machine (SVM) to re-train the classifier using only the new data. Granular computing makes it adaptive to local and global variations in data distribution, while soft labels provide resilience to noise. Each time data is acquired, new support vectors that are linearly independent are added and existing support vectors that do not improve the classifier performance are removed. This constrains the size of the support vectors and significantly reduces the training time without compromising the classification accuracy. The efficacy of the proposed online learning strategy is validated in a near infrared face verification application involving different covariates. The results obtained on a heterogeneous near infrared face database of 328 subjects show that in all experiments using different feature extraction and classification algorithms the proposed online 2 ν -Granular Soft Support Vector Machine learning approach is 2–3 times faster while achieving a high level of accuracy similar to offline training using all data.

Improving the accuracy and storage cost in biometric remote authentication schemes

Recently, Bringer et al. proposed a new approach for remote biometric based verification, which consists of a hybrid protocol that distributes the server side functionality in order to detach the biometric data storage from the service provider. Besides, a new security model is defined using the notions of Identity and Transaction Privacy, which guarantee the privacy of the identity-biometrics relationship under the assumption of non-colluding servers. However, due to the high communication and computational costs, the systems following this model cannot be implemented for large scale biometric systems. In this paper, we describe an efficient multi-factor biometric verification system with improved accuracy and lower complexity by considering the range information of every component of the user biometrics separately. Also, the new scheme is provably secure based on the security model of Bringer et al and implements a different database storage that eliminates the disadvantages of encrypted biometric templates in terms of ciphertext expansion. Also, we evaluate different Private Information Retrieval (PIR) schemes applicable for this setting and propose a practical solution for our scheme that reduces the computation costs dramatically. Finally, we compare our results with existing provably secure schemes and achieve reduced computational cost and database storage cost due to the single storage of the common features of the users in the system and amortization of the time complexity of the PIR.