Multimodal Biometric Identification System Research Articles

Towards an Intelligent Multimodal Biometric Identification System

the goal of this project is to bring together and integrates the work of the laboratory team members in order to get a practical realization of an Intelligent Multimodal Biometric Identification System. This last exploits the vocal and visual properties of a person to carry out, her or his, identification. To improve classification performance, the system will have a virtual character module which can exchange information with the person to be identified according to a random survey (based on information provided previously). Our first objective is to equip our laboratory with a powerful access control system able to identify the members of the lab and follows them where ever they go helping them with all kind of information they may be in need to .

Biometric template protection in multimodal authentication systems based on error correcting codes*

The widespread deployment of biometric systems has raised public concern about security and privacy of personal data. In this paper, we present a novel framework for biometric template security in multimodal biometric authentication systems based on error correcting codes. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder based on distributed source coding principles. It is shown that the proposed method binds the biometric template in a cryptographic key which does not reveal any information about the original biometric data even if it is compromised by an attacker. Furthermore, the advantages of the proposed method in terms of security and impact on matching accuracy are discussed. We assess the performance of the proposed method in the context of HUMABIO, an EU Specific Targeted Research Project, where face and gait biometrics are employed in an unobtrusive application scenario for human authentication. Experimental evaluation on a multimodal biometric database demonstrates the validity of the proposed method.

A MULTIMODAL BIOMETRIC IDENTIFICATION SYSTEM USING COMPRESSED FINGER IMAGES

Finger image recognition remains one of the most prominent biometric identification methods. However, storage of finger databases needs allocation of huge secondary storage devices. In addition, there has been limited success in obtaining a satisfactory system due to the complexity of the problem. In this article, a low-cost, high-speed, multimodal biometric identification system trained with compressed finger images is presented with the objective to increase the overall matching confidence level. For this, three finger images of the left or right hand, or both, for one person are matched and the output decision is combined. A prototype optical-based finger-data acquisition system using the CCD (charge coupled device) digital still camera is adopted to capture a complete impression of finger area required for accurately identifying an individual. The acquired images then are compressed with a Coif5 wavelet packet-based scheme to increase the overall performance and eliminate bulk storage requirements. The finger image features are extracted with an adaptive neural network for the implementation of a three-finger multimodal system to achieve a peak identification rate of 100% (99.4% on average) in 0.15s for a database of 50 persons and 450 test images.

A biometric identification system based on eigenpalm and eigenfinger features

This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).

Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems

We examine the performance of multimodal biometric authentication systems using state-of-the-art Commercial Off-the-Shelf (COTS) fingerprint and face biometric systems on a population approaching 1,000 individuals. The majority of prior studies of multimodal biometrics have been limited to relatively low accuracy non-COTS systems and populations of a few hundred users. Our work is the first to demonstrate that multimodal fingerprint and face biometric systems can achieve significant accuracy gains over either biometric alone, even when using highly accurate COTS systems on a relatively large-scale population. In addition to examining well-known multimodal methods, we introduce new methods of normalization and fusion that further improve the accuracy.

Intelligent Transportation Systems

This issues Works in Progress department features 10 interesting ongoing intelligent transportation systems projects. The first five projects (TIME, Sentient Transport, EVT, DynaCHINA, TrafficView) focus on traffic and vehicular data collection, transmission, and analysis. The sixth projects aims to provide intelligent-copilot services for driven, while the seventh focuses on asset identification and data collection for railroad environments. The eighth and ninth projects are budding application development environments for automobiles, and the tenth project is designing a multimodal biometric identification system for travel documents