The latest research indicates using a combination of biometric avenues for human identification is more effective, and far more challenging.

Biometric systems that use a single biometric trait have to contend with noisy data, restricted degrees of freedom, failureto -enroll problems, spoof attacks, and unacceptable error rates. Multibiometric systems that use multiple traits of an individual for authentication, alleviate some of these problems while improving verification performance. We demonstrate that the performance of multibiometric systems can be further improved by learning user-specific parameters. Two types of parameters are considered here. (i) Thresholds that are used to decide if a matching score indicates a genuine user or an impostor, and (ii) weights that are used to indicate the importance of matching scores output by each biometric trait. User-specific thresholds are computed using the cumulative histogram of impostor matching scores corresponding to each user. The user-specific weights associated with each biometric are estimated by searching for that set of weights which minimizes the total verification error. The tests were conducted on a database of 50 users who provided fingerprint, face and hand geometry data, with 10 of these users providing data over a period of two months. We observed that user-specific thresholds improved system performance by 2%, while user-specific weights improved performance by 3%.

In this paper we review the major approaches to Multimodal Human Computer Interaction, giving an overview of the field from a computer vision perspective. In particular, we focus on body, gesture, gaze, and affective interaction (facial expression recognition and emotion in audio). We discuss user and task modeling, and multimodal fusion, highlighting challenges, open issues, and emerging applications for Multimodal Human Computer Interaction (MMHCI) research.

The elements of multimodal authentication along with system models are presented. These include the machine experts as well as machine supervisors. In particular fingerprint and speech based systems will serve as illustration of a mobile authentication application. A novel signal adaptive supervisor, based on the input biometric signal quality is evaluated. Experimental results on data collected from mobile telephones are reported demonstrating the benefits of the proposed scheme .

We design, implement, and evaluate a technique to identify the source network interface card (NIC) of an IEEE 802.11 frame through passive radio-frequency analysis. This technique, called PARADIS, leverages minute imperfections of transmitter hardware that are acquired at manufacture and are present even in otherwise identical NICs. These imperfections are transmitter-specific and manifest themselves as artifacts of the emitted signals. In PARADIS, we measure differentiating artifacts of individual wireless frames in the modulation domain, apply suitable machine-learning classification tools to achieve significantly higher degrees of NIC identification accuracy than prior best known schemes. We experimentally demonstrate effectiveness of PARADIS in differentiating between more than 130 identical 802.11 NICs with accuracy in excess of 99%. Our results also show that the accuracy of PARADIS is resilient against ambient noise and fluctuations of the wireless channel. Although our implementation deals exclusively with IEEE 802.11, the approach itself is general and will work with any digital modulation scheme. This research was performed under an appointment to the

this article, we outline the state-of-the-art of several popular biometric modalities and technologies and provide specific applications where biometric recognition may be beneficially incorporated. In addition, we discuss integration strategies of biometric authentication technologies into DRM systems so that the whole process meets the needs and requirements of consumers, content providers, and payment brokers, securing delivery channels and contents

Reliable person identification is an important problem in diverse businesses. Biometrics, identification based on distinctive personal traits, has the potential to become an irreplaceable part of any identification system. While successful in some niche markets, the biometrics technology has not yet delivered its promise of foolproof automatic identification. With the availability of inexpensive biometric sensors and computing power, it is becoming increasingly clear that widespread usage of biometric person identification is being stymied by our lack of understanding of three fundamental problems: (i) How to accurately and efficiently represent and recognize biometric patterns? (ii) How to guarantee that the sensed measurements are not fraudulent? and (iii) How to make sure that the application is indeed exclusively using pattern recognition for the expressed purpose (function creep [16])? Solving these core problems will not only catapult biometrics into mainstream applications but will also stimulate adoption of other pattern recognition applications for providing effective automation of sensitive tasks without jeopardizing our individual freedoms. For these reasons, we view biometrics as a grand challenge- "a fundamental problem in science and engineering with broad economic and scientific impact 1 ". 1.

Abstract. The effect of image quality on the performance of fingerprint verification is studied. In particular, we investigate the performance of two fingerprint matchers based on minutiae and ridge information as well as their score-level combination under varying fingerprint image quality. The ridge-based system is found to be more robust to image quality degradation than the minutiae-based system. We exploit this fact by introducing an adaptive score fusion scheme based on automatic quality estimation in the spatial frequency domain. The proposed scheme leads to enhanced performance over a wide range of fingerprint image quality. 1

We examine the performance of multimodal biometric authentication systems using state-of-the-art Commercial Off-the-Shelf (COTS) fingerprint and face biometrics on a population approaching 1000 individuals. Prior studies of multimodal biometrics have been limited to relatively low accuracy non-COTS systems and populations approximately 10 % of this size. 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 already highly accurate COTS systems on a relatively large-scale population. In addition to examining well-known multimodal methods, we introduce novel methods of fusion and normalization that improve accuracy still further through population analysis. 1.

Identity verification systems are an important part of our every day life. A typical example is the Automatic Teller Machine (ATM) which employs a simple identity verification scheme: the user is asked to enter their secret password after inserting their ATM card; if the password matches the one prescribed to the card, the user is allowed access to their bank account. This scheme suffers from a major drawback: only the validity of the combination of a certain possession (the ATM card) and certain knowledge (the password) is verified. The ATM card can be lost or stolen, and the password can be compromised. Thus new verification methods have emerged, where the password has either been replaced by, or used in addition to, biometrics such as the person's speech, face image or fingerprints. Apart from the ATM example described above, biometrics can be applied to other areas, such as telephone & internet based banking, airline reservations & check-in, as well as forensic work and law enforcement applications. Biometric systems