Enhancing Privacy for Biometric Identification Cards

Most developed countries have started the implementation of biometric electronic identification cards, especially passports. The European Union and the United States of America struggle to introduce and standardize these electronic documents. Due to the personal nature of the biometric elements used for the generation of these cards, privacy issues were raised on both sides of the Atlantic Ocean, leading to civilian protests and concerns. The lack of transparency from the public authorities responsible with the implementation of such identification systems, and the poor technological approaches chosen by these authorities, are the main reasons for the negative popularity of the new identification methods. The following article shows an approach that provides all the benefits of modern technological advances in the fields of biometrics and cryptography, without sacrificing the privacy of those that will be the beneficiaries of the new systemsecurity, smart card, identification, passport, biometrics, public key infrastructure, government, identification

Biometric Identification using Phonocardiogram

Phonocardiogram (PCG) signals as a biometric is a new and novel method for user identification. Use of PCG signals for user recognition is a highly reliable method because heart sounds are produced by internal organs and cannot be forged easily as compared to other recognition systems such as fingerprint, iris, DNA etc. PCG signals have been recorded using an electronic stethoscope. Database of heart sound is made using the electronic stethoscope. In the beginning, heart sounds for different classes is observed in time as well as frequency for their uniqueness for each class. The first step performed is to extract features from the recorded heart signals. We have implemented LFBC algorithm as a feature extraction algorithm to get the cepstral component of heart sound. The next objective is to classify these feature vectors to recognize a person. A classification algorithm is first trained using a training sequence for each user to generate unique features for each user. During the testing period, the classifier uses the stored training attributes for each user and uses them to match or identify the testing sequence. We have used LBG-VQ and GMM for the classification of user classes. Both the algorithms are iterative, robust and well established methods for user identification. We have implemented the normalization at two places; first, before feature extraction; then just after the feature extraction in case of GMM classifier which is not proposed in earlier literature

Roman portraiture and biometric identification

This project utilised three-dimensional scanning technology in the study of ancient Roman art and archaeology: Roman representations of faces executed in marble. In the cultural heritage sector, three-dimensional (3D) scanning finds its primary application in documenting and reconstructing objects and structures mostly of simple geometry: bones, pottery, architecture or the imprint of whole archaeological sites (Adolf 2011). In forensic science, the face is interesting from investigative and probative perspectives, including both recognition and identification. Biometric methods of facial recognition have been part of a plethora of computer science-based applications used in the verification of identity (Davy et al. 2005, Goodwin, Evison and Schofield 2010). The aim of this initial project is to provide objective relevant measurements of key facial features from the two ancient Roman portrait statue three-dimensional scans, which will allow the delineation of relationships between individual portraits including formal and stylistics aspects. The work described in this paper proposal is truly multidisciplinary, it touches on many fields including : Classical archaeologies (specifically ancient art history in the period of the Roman Empire 31BC - AD400), Forensic Anthropology (specifically physical anthropology and human osteology, Facial Biometrics (specifically uniquely recognising humans based upon their intrinsic physical traits and features) and Computer Science and Statistics (specifically the analysis of large complex multi-dimensional data sets)