This study contributes to developing the concept of decision-making support in biometric-based situational awareness systems. Such systems assist users in gathering and analysing biometric data, and support the decision-making on the human behavioural pattern and/or authentication. As an example, the authors consider a facial biometric assistant that functions based on multi-spectral biometrics in visible and infrared bands; it involves facial expression recognition, face recognition in both spectra, as well as estimation of physiological parameters. The authors also investigate usage of facial biometrics for the semantic representation for advanced decision-making.

Fuzzy vault is the most practical scheme in bio-cryptosystems for the applications in protecting data in the real-world, error-prone environments. The biometric features were used to lock and unlock the secret key, which is encoded in the coefficients of a polynomial equation. The security of the fuzzy vault depends on the infeasibility of the polynomial reconstruction problem. The vault performance can be enhanced by adding more noise (chaff) points to the vault. The existing methods for generating chaff points were time consuming as producing more than 200 chaff points. This paper proposes a new chaff point generation technique for the fuzzy vault in bio-cryptosystems which is less time-consuming for producing more than 200 points. Complexity study shows that our algorithm has a complexity of O(n ²), which is a significant improvement over the existing algorithm of the complexity of O(n ³). Our experimental results show that the proposed algorithm achieves 14.84 and 41.86 times faster than Clancy's and Khalil-Hani's algorithms in the case of generating 240 chaff points. To generate the same numbers of valid chaff points, our proposed method needs less candidate points than the existing methods. Our proposed algorithm generates 11% more chaff points compared to the Khalil-Hani's algorithm.

Dentistry can contribute for the identification of human remains after any disasters or crimes in assistance to other medical specialties. The algorithm can be developed by comparing post mortem and ante mortem dental radiographs. This work aims to introduce photographic images in addition to radiographs. In this research a contour and skeleton-based shape extraction as well as matching algorithm for dental images is proposed. An active contour model with selective binary and Gaussian filtering regularised level set method is used for contour extraction. Shape matching is done by both contour and skeleton-based approaches. The experimental results are obtained from a database of dental images include both radiographs and photographs. This algorithm provides better matching decision about the person than the existing algorithms since it includes skeleton measures also. The performance measures obtained and the hit-rate indicates that the better matching is observed with radiographic than the photographic images.

The electrocardiogram (ECG) is a non-invasive and widely used technique for cardiac electrophysiological assessment. Although the ECG has traditionally only been used for functional diagnostic and evaluation, several advances in electrophysiological sensing have made available robust signal acquisition devices, particularly suited for ambulatory conditions, widening its range of applications. In particular, recent work has shown the potential of the ECG as a biometric trait, both for human identification and authentication. This study sets the ground for an ECG-based real-time biometric system. The authors describe an experimental setup and the evaluation of new fiducial and non-fiducial approaches, including data acquisition, signal processing, feature extraction and analysis and classification methodologies, showing the applicability of the ECG as a real-time biometric. Performance evaluation was done in clinical-grade ECG recording from 51 healthy control individuals (of a publicly available benchmark dataset) as well as on data collected from 26 healthy volunteers performing computer activities without any posture or motion limitations, thus simulating a regular computer usage scenario.

The storage of fingerprints is an important issue as this biometric modality is more and more deployed for real applications. Considering minutiae templates as sensitive information, a key question concerns the secure and privacy management of this digital identity. Indeed, if an attacker obtains the minutiae template of a subject, he/she will be able to generate a fingerprint having the same characteristics. Instead of directly storing the minutiae templates, the authors propose in this study a new adaptation of BioHashing to generate a cancellable template in the context of un-ordered set of noisy minutiae features. To the authors knowledge, little interest has been paid in the literature to the question of protecting minutiae template by BioHashing until now. Using the FVC2002 benchmark database, they show the effectiveness of the proposed approach in term of privacy preservation. They show how the proposed method copes with irreversibility and diversity properties and therefore can be efficient in a realistic context.