Investigation into DCT Feature Selection for Visual Lip-Based Biometric Authentication

This paper investigated using lip movements as a behavioural biometric for person authentication. The system was trained, evaluated and tested using the XM2VTS dataset, following the Lausanne Protocol configuration II. Features were selected from the DCT coefficients of the greyscale lip image. This paper investigated the number of DCT coefficients selected, the selection process, and static and dynamic feature combinations. Using a Gaussian Mixture Model - Universal Background Model framework an Equal Error Rate of 2.20% was achieved during evaluation and on an unseen test set a False Acceptance Rate of 1.7% and False Rejection Rate of 3.0% was achieved. This compares favourably with face authentication results on the same dataset whilst not being susceptible to spoofing attacks.

FPGA-based minutia matching for biometric fingerprint image database retrieval

In this paper, a parallel-matching processor architecture with early jump-out (EJO) control is proposed to carry out high-speed biometric fingerprint database retrieval. The processor performs the fingerprint retrieval by using minutia point matching. An EJO method is applied to the proposed architecture to speed up the large database retrieval. The processor is implemented on a Xilinx Virtex-E, and occupies 6,825 slices and runs at up to 65 MHz. The software/hardware co-simulation benchmark with a database of 10,000 fingerprints verifies that the matching speed can achieve the rate of up to 1.22 million fingerprints per second. EJO results in about a 22% gain in computing efficiency.

Performance Analysis of Novel Randomly Shifted Certification Authority Authentication Protocol for MANETs

The provision of security in mobile ad hoc networks is of paramount importance due to their wireless nature. However, when conducting research into security protocols for ad hoc networks it is necessary to consider these in the context of the overall system. For example, communicational delay associated with the underlying MAC layer needs to be taken into account. Nodes in mobile ad hoc networks must strictly obey the rules of the underlying MAC when transmitting security-related messages while still maintaining a certain quality of service. In this paper a novel authentication protocol, RASCAAL, is described and its performance is analysed by investigating both the communicational-related effects of the underlying IEEE 802.11 MAC and the computational-related effects of the cryptographic algorithms employed. To the best of the authors' knowledge, RASCAAL is the first authentication protocol which proposes the concept of dynamically formed short-lived random clusters with no prior knowledge of the cluster head. The performance analysis demonstrates that the communication losses outweigh the computation losses with respect to energy and delay. MAC-related communicational effects account for 99% of the total delay and total energy consumption incurred by the RASCAAL protocol. The results also show that a saving in communicational energy of up to 12.5% can be achieved by changing the status of the wireless nodes during the course of operation. Copyright (C) 2009 G. A. Safdar and M. P. O'Neill (nee McLoone).

Multimodal Biometric Human Recognition for Perceptual Human-Computer Interaction

In this paper, a novel video-based multimodal biometric verification scheme using the subspace-based low-level feature fusion of face and speech is developed for specific speaker recognition for perceptual human--computer interaction (HCI). In the proposed scheme, human face is tracked and face pose is estimated to weight the detected facelike regions in successive frames, where ill-posed faces and false-positive detections are assigned with lower credit to enhance the accuracy. In the audio modality, mel-frequency cepstral coefficients are extracted for voice-based biometric verification. In the fusion step, features from both modalities are projected into nonlinear Laplacian Eigenmap subspace for multimodal speaker recognition and combined at low level. The proposed approach is tested on the video database of ten human subjects, and the results show that the proposed scheme can attain better accuracy in comparison with the conventional multimodal fusion using latent semantic analysis as well as the single-modality verifications. The experiment on MATLAB shows the potential of the proposed scheme to attain the real-time performance for perceptual HCI applications.

Bimodal biometric verification based on face and lips

In this paper, we present a novel approach to person verification by fusing face and lip features. Specifically, the face is modeled by the discriminative common vector and the discrete wavelet transform. Our lip features are simple geometric features based on a lip contour, which can be interpreted as multiple spatial widths and heights from a center of mass. In order to combine these features, we consider two simple fusion strategies: data fusion before training and score fusion after training, working with two different face databases. Fusing them together boosts the performance to achieve an equal error rate as low as 0.4% and 0.28%, respectively, confirming that our approach of fusing lips and face is effective and promising.

Targeted Impersonation As A Tool For The Detection Of Biometric System Vulnerabilities

This paper argues that biometric verification evaluations can obscure vulnerabilities that increase the chances that an attacker could be falsely accepted. This can occur because existing evaluations implicitly assume that an imposter claiming a false identity would claim a random identity rather than consciously selecting a target to impersonate. This paper shows how an attacker can select a target with a similar biometric signature in order to increase their chances of false acceptance. It demonstrates this effect using a publicly available iris recognition algorithm. The evaluation shows that the system can be vulnerable to attackers targeting subjects who are enrolled with a smaller section of iris due to occlusion. The evaluation shows how the traditional DET curve analysis conceals this vulnerability. As a result, traditional analysis underestimates the importance of an existing score normalisation method for addressing occlusion. The paper concludes by evaluating how the targeted false acceptance rate increases with the number of available targets. Consistent with a previous investigation of targeted face verification performance, the experiment shows that the false acceptance rate can be modelled using the traditional FAR measure with an additional term that is proportional to the logarithm of the number of available targets.

TARGETED BIOMETRIC IMPERSONATION

When applying biometric algorithms to forensic verification, false acceptance and false rejection can mean a failure to identify a criminal, or worse, lead to the prosecution of individuals for crimes they did not commit. It is therefore critical that biometric evaluations be performed as accurately as possible to determine their legitimacy as a forensic tool. This paper argues that, for forensic verification scenarios, traditional performance measures are insufficiently accurate. This inaccuracy occurs because existing verification evaluations implicitly assume that an imposter claiming a false identity would claim a random identity rather than consciously selecting a target to impersonate. In addition to describing this new vulnerability, the paper describes a novel Targeted.. FAR metric that combines the traditional False Acceptance Rate (FAR) measure with a term that indicates how performance degrades with the number of potential targets. The paper includes an evaluation of the effects of targeted impersonation on an existing academic face verification system. This evaluation reveals that even with a relatively small number of targets false acceptance rates can increase significantly, making the analysed biometric systems unreliable.

Fingerprint Volatile Organic Compounds (VOC) analysis in Goat’s Milk and Halloumi Cheese as Marker for Authentication of Region of Origin

Goats’ milk is responsible for unique traditional products such as Halloumi cheese. The characteristics of Halloumi depend on the original features of the milk and on the conditions under which the milk has been produced such as feeding regime of the animals or region of production. Using a range of milk (33) and Halloumi (33) samples collected over a year from three different locations in Cyprus (A, Anogyra; K, Kofinou; P, Paphos), the potential for fingerprint VOC analysis as marker to authenticate Halloumi was investigated. This unique set up consists of an in-injector thermo desorption (VOCtrap needle) and a chromatofocusing system based on mass spectrometry (VOCscanner). The mass spectra of all the analyzed samples are treated by multivariate analysis (Principle component analysis and Discriminant functions analysis). Results showed that the highland area of product (P) is clearly identified in milks produced (discriminant score 67%). It is interesting to note that the higher similitude found on milks from regions “A” and “K” (with P being distractive; discriminant score 80%) are not ‘carried over’ on the cheeses (higher similitude between regions “A” and “P”, with “K” distinctive). Data have been broken down into three seasons. Similarly, the seasonality differences observed in different milks are not necessarily reported on the produced cheeses. This is expected due to the different VOC signatures developed in cheeses as part of the numerous biochemical changes during its elaboration compared to milk. VOC however it is an additional analytical tool that can aid in the identification of region origin in dairy products.

The Impact of EU Privacy Legislation on Biometric System Deployment: Protecting citizens but constraining applications

Biometric systems provide a valuable service in helping to identify individuals from their stored personal details. Unfortunately, with the rapidly increasing use of such systems, there is a growing concern about the possible misuse of that information. To counteract the threat, the European Union (EU) has introduced comprehensive legislation that seeks to regulate data collection and help strengthen an individual’s right to privacy. This article looks at the implications of the legislation for biometric system deployment. After an initial consideration of current privacy concerns, it examines what is meant by ‘personal data’ and its protection, in legislation terms. Also covered are issues around the storage of biometric data, including its accuracy, its security, and justification for what is collected. Finally, the privacy issues are illustrated through three biometric use cases: border security, online bank access control and customer profiling in stores.