An Advanced Technique for User Identification Using Partial Fingerprint

User identification is a very interesting and complex task. Invasive biometrics is based on traits uniqueness and immutability over time. In forensic field, fingerprints have always been considered an essential element for personal recognition. The traditional issue is focused on full fingerprint images matching. In this paper an advanced technique for personal recognition based on partial fingerprint is proposed. This system is based on fingerprint local analysis and micro-features, endpoints and bifurcations, extraction. The proposed approach starts from minutiae extraction from a partial fingerprint image and ends with the final matching score between fingerprint pairs. The computation of likelihood ratios in fingerprint identification is computed by trying every possible overlapping of the partial image with complete image. The first experimental results conducted on the PolyU (Hong Kong Polytechnic University) free database show an encouraging performance in terms of identification accuracy

An Embedded Biometric Sensor for Ubiquitous Authentication

Communication networks and distributed technologies move people towards the era of ubiquitous computing. An ubiquitous environment needs many authentication sensors for users recognition, in order to provide a secure infrastructure for both user access to resources and services and information management. Today the security requirements must ensure secure and trusted user information to protect sensitive data resource access and they could be used for user traceability inside the platform. Conventional authentication systems, based on username and password, are in crisis since they are not able to guarantee a suitable security level for several applications. Biometric authentication systems represent a valid alternative to the conventional authentication systems providing a flexible einfrastructure towards an integrated solution supporting the requirement for improved inter-organizational functionality. In this work the study and the implementation of a fingerprintsbased embedded biometric system is proposed. Typical strategies implemented in Identity Management Systems could be useful to protect biometric information. The proposed sensor can be seen as a self-contained sensor: it performs the all elaboration steps on board, a necessary requisite to strengthen security, so that sensible data are securely managed and stored inside the sensor, without any data leaking out. The sensor has been prototyped via an FPGA-based platform achieving fast execution time and a good final throughput. Resources used, elaboration times of the sensor are reported. Finally, recognition rates of the proposed embedded biometric sensor have been evaluated considering three different databases: the FVC2002 reference database, the CSAI/Biometrika proprietary database, and the CSAI/Secugen proprietary database. The best achieved FAR and FRR indexes are respectively 1.07% and 8.33%, with an elaboration time of 183.32 ms and a working frequency of 22.5 MHz

Embedded Knowledge-based Speech Detectors for Real-Time Recognition Tasks

Speech recognition has become common in many application domains, from dictation systems for professional practices to vocal user interfaces for people with disabilities or hands-free system control. However, so far the performance of automatic speech recognition (ASR) systems are comparable to human speech recognition (HSR) only under very strict working conditions, and in general much lower. Incorporating acoustic-phonetic knowledge into ASR design has been proven a viable approach to raise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as detectors for manner of articulation attributes starting from representations of speech signal frames. In this paper, the full system implementation is described. The system has a first stage for MFCC extraction followed by a second stage implementing a sinusoidal based multi-layer perceptron for speech event classification. Implementation details over a Celoxica RC203 board are give