Vincenzo Conti

A Frequency-based Approach for Features Fusion in Fingerprint and Iris Multimodal Biometric Identification Systems

The basic aim of a biometric identification system is to discriminate automatically between subjects in a reliable and dependable way, according to a specific-target application. Multimodal biometric identification systems aim to fuse two or more physical or behavioral traits to provide optimal False Acceptance Rate (FAR) and False Rejection Rate (FRR), thus improving system accuracy and dependability. In this paper, an innovative multimodal biometric identification system based on iris and fingerprint traits is proposed. The paper is a state-of-the-art advancement of multibiometrics, offering an innovative perspective on features fusion. In greater detail, a frequency-based approach results in a homogeneous biometric vector, integrating iris and fingerprint data. Successively, a hamming-distance-based matching algorithm deals with the unified homogenous biometric vector. The proposed multimodal system achieves interesting results with several commonly used databases. For example, we have obtained an interesting working point with FAR = 0% and FRR = 5.71% using the entire fingerprint verification competition (FVC) 2002 DB2B database and a randomly extracted same-size subset of the BATH database. At the same time, considering the BATH database and the FVC2002 DB2A database, we have obtained a further interesting working point with FAR = 0% and FRR = 7.28% ÷ 9.7%.

An extended JADE-S based framework for developing secure Multi-Agent Systems

Agent communities are self-organized virtual spaces consisting of a large number of agents and their dynamic environments. Within a community, agents group together offering special e-services for effective, reliable, and mutual benefits. Usually, an agent community is composed of specialized agents performing one or more tasks in a single domain/sub-domain, or in highly intersecting domains. However, secure Multi-Agent Systems require severe mechanisms in order to prevent malicious attacks. Several limits affect exiting secure agents platform, such as the lack of a strong authentication system, the lack of a flexible distributed mechanism for access control and the lack of a system for storing past behaviors of agent/user. Biometric owner agents authentication, agent/users policies to regulate agents behavior and actions, and agent/users reputation level to select trusted agents can be used to overcome the above limits and enhance the level of security for these applications. In this paper an extended JADE-S based framework for developing secure Multi-Agent Systems is proposed. The framework functionalities are extended by self-contained FPGA biometric sensors providing secure and fast user authentication service. Each agent owner, by means of biometric authentication, acquires his/her own X.509v3 digital certificate. Policy files and a flexible, fast distributed Access Control Mechanism can regulate behavior and actions of any users/agent inside the platform. In addition, a mechanism based on the agent reputation is used: reputation is an attribute associated to each owner and/or agent on the basis of its past behavior and integrity. In order to prove the feasibility of the proposed framework, we have developed a multi-agent e-Banking system. System goal deals with e-Banking services such as bank account statements, account transactions and so on. In the paper, the experimental features of the biometric self-contained sensors are also outlined.

Introducing Pseudo-Singularity Points for Efficient Fingerprints Classification and Recognition

Fingerprint classification and matching are two key issues in automatic fingerprint recognition. Generally, fingerprint recognition is based on a set of relevant local characteristics, such as ridge ending and bifurcation (minutiae). Fingerprint classification is based on fingerprint global features, such as core and delta singularity points. Unfortunately, singularity points are not always present in a fingerprint image: the acquisition process is not ideal, so that the fingerprint is broken, or the fingerprint belongs to the arch class. In the above cases, pseudo-singularity-points will be detected and extracted to make possible fingerprint classification and matching. As result, fingerprint processing involves few steps and, in the same way, fingerprint matching involves the comparison of few features with recognition rates comparable to the standard minutiae based systems. The experiments trials have been conducted on many official Fingerprint Verification Competition (FVC) databases. The achieved results show the effectiveness of the proposed approach, obtaining a False Acceptance Rate (FAR) = 1.22% and a False Rejection Rate (FRR) = 9.23% with FVC2002 DB2-A database. In the best of case, a FAR=0.26% and a FRR=7.36% with FVC2000 DB1-B database is achieved. To the best of our knowledge, this is the first recognition system based only on singularity regions.

Fuzzy fusion in multimodal biometric systems

Multimodal authentication systems represent an emerging trend for information security. These systems could replace conventional mono-modal biometric methods using two or more features for robust biometric authentication tasks. They employ unique combinations of measurable physical characteristics: fingerprint, facial features, iris of the eye, voice print, hand geometry, vein patterns, and so on. Since these traits are hardly imitable by other persons, the aim of these multibiometric systems is to achieve a high reliability to determine or verify persons identity. In this paper a multimodal biometric system using two different fingerprints is proposed. The matching module integrates fuzzy logic methods for matching score fusion. Experimental trials using both decision level fusion and matching score level fusion were performed. Experimental results show an improvement of 6.7% using the matching score level fusion rather then a mono-modal authentication system.

A Novel Embedded Fingerprints Authentication System Based on Singularity Points

In this paper a novel embedded fingerprints authentication system based on core and delta singularity points detection is proposed. Typical fingerprint recognition systems use core and delta singularity points for classification tasks. On the other hand, the available optical and photoelectric sensors give high quality fingerprint images with well defined core and delta points, if they are present. In the proposed system, fingerprint matching is based on singularity points position, orientation, and relative distance detection. As result, fingerprint matching involves the comparison between few features leading to a very fast system with recognition rates comparable to the standard minutiae based recognition systems. The whole system has been prototyped on the Celoxica RC203E board, equipped with a Xilinx VirtexII FPGA. The prototype has been tested with the FVC databases. Hardware system performance show high recognition rates and low execution time: FAR = 1.2% and FRR=2.6%, while each fingerprint elaboration requires 34.82 ms. Considering both FAR, FRR indexes and the high speed, the proposed system could be used for identification tasks in large fingerprint databases. To the best of our knowledge, this is the first authentication system based on singularity points.

A multimodal technique for an embedded fingerprint recognizer in mobile payment systems

The development and the diffusion of distributed systems, directly connected to recent communication technologies, move people towards the era of mobile and ubiquitous systems. Distributed systems make merchant-customer relationships closer and more flexible, using reliable e-commerce technologies. These systems and environments need many distributed access points, for the creation and management of secure identities and for the secure recognition of users. Traditionally, these access points can be made possible by a software system with a main central server. This work proposes the study and implementation of a multimodal technique, based on biometric information, for identity management and personal ubiquitous authentication. The multimodal technique uses both fingerprint micro features (minutiae) and fingerprint macro features (singularity points) for robust user authentication. To strengthen the security level of electronic payment systems, an embedded hardware prototype has been also created: acting as self-contained sensors, it performs the entire authentication process on the same device, so that all critical information (e.g. biometric data, account transactions and cryptographic keys), are managed and stored inside the sensor, without any data transmission. The sensor has been prototyped using the Celoxica RC203E board, achieving fast execution time, low working frequency, and good recognition performance.

Efficient MLP digital implementation on FPGA

The efficiency and the accuracy of a digital feedforward neural networks must be optimized to obtain both high classification rate and minimum area on chip. In this paper an efficient MLP digital implementation. The key features of the hardware implementation are the virtual neuron based architecture and the use of the sinusoidal activation function for the hidden layer. The effectiveness of the proposed solutions has been evaluated developing different FPGA based neural prototypes for the high energy physics domain and the automatic road sign recognition domain. The use of the sinusoidal activation function decreases hardware resource employment of about 32% when compared with the standard sigmoid based neuron implementation. The virtual neuron implementation makes efficient the mapping of a neural network into hardware devices since it leads to a significant decreasing of concurrent memory access.

A Self-Contained Biometric Sensor for Ubiquitous Authentication

Development and diffusion of embedded systems, directly connected to communication technologies, move people towards the era of ubiquitous computing. An ubiquitous environment needs of many self-contained authentication sensors, opportunely distributed, for users recognition and their secure access. In this work the study and the implementation of a fingerprints-based embedded biometric system for personal ubiquitous authentication is proposed. The system is a self-contained sensor since it is able to perform fingerprint acquisition and processing for user authentication, to strengthen security: the processor performs the entire elaboration steps on board, so that all critical information (i.e. biometric data and cryptographic keys), are securely managed and stored inside the sensor, without any data leaking out. Sensor has been realized on a FPGA-based platform achieving fast execution time and a good final throughput. Resources used, elaboration times and recognition rates of the sensor are finally reported.

An intelligent sensor for fingerprint recognition

In this paper an intelligent sensor for fingerprint recognition is proposed. The sensor has the objective to overcome some limits of the fingerprint recognition software systems, as elaboration time and security issues related to fingerprint transmission between sensor and processing unit. Intelligent sensor has been prototyped using the Hamster Secugen sensor for image acquisition and the Celoxica RC1000 board, employing a Xilinx VirtexE2000 FPGA, for image processing and analysis. Resources used, elaboration time as well the recognition rates in both verification and identification modes are reported in the paper. To the best of our knowledge, this is the first implementation for a full hardware implemented fingerprint recognition system.

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.