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Competing notions of biometric recognition system failure prediction have emerged recently, which can roughly be categorized as quality and non-quality based approaches. Quality, while well correlated overall with recognition performance, is a weaker indication of how the system will perform in a particular instance - something of primary importance for critical installations, screening areas, and surveillance posts. An alternative approach, incorporating a failure prediction receiver operator characteristic (FPROC) analysis has been proposed to overcome the limitations of the quality approach, yielding accurate predictions on a per instance basis. In this paper, we develop a full multi-modal recognition system integrating an FPROC fusion-based failure prediction engine. Four different fusion techniques to enhance failure prediction are developed and evaluated for this system. We present results for the NIST BSSR1 multi-modal data set, and a larger "chimera" set also composed of data from BSSR1. Our results show a significant improvement in recognition performance with the fusion approach, over the baseline recognition results and previous fusion approaches.

This paper is a security analysis of leading privacy enhanced technologies (PETs) for biometrics including biometric fuzzy vaults (BFV) and biometric encryption (BE). The lack of published attacks, combined with various "proven" security properties has been taken by some as a sign that these technologies are ready for deployment. While some of the existing BFV and BE techniques do have "proven" security properties, those proofs make assumptions that may not, in general, be valid for biometric systems. We briefly review some of the other known attacks against BFV and BE techniques. We introduce three disturbing classes of attacks against PET techniques including attack via record multiplicity, surreptitious key-inversion attack, and novel blended substitution attacks. The paper ends with a discussion of the requirements for an architecture to address the privacy and security requirements.

This paper reviews the biometric dilemma, the pending threat that may limit the long-term value of biometrics in security applications. Unlike passwords, if a biometric database is ever compromised or improperly shared, the underlying biometric data cannot be changed. The concept of revocable or cancelable biometric-based identity tokens (biotokens), if properly implemented, can provide significant enhancements in both privacy and security and address the biometric dilemma. The key to effective revocable biotokens is the need to support the highly accurate approximate matching needed in any biometric system as well as protecting privacy/security of the underlying data. We briefly review prior work and show why it is insufficient in both accuracy and security. This paper adapts a recently introduced approach that separates each datum into two fields, one of which is encoded and one which is left to support the approximate matching. Previously applied to faces, this paper uses this approach to enhance an existing fingerprint system. Unlike previous work in privacy-enhanced biometrics, our approach improves the accuracy of the underlying svstem! The security analysis of these biotokens includes addressing the critical issue of protection of small fields. The resulting algorithm is tested on three different fingerprint verification challenge datasets and shows an average decrease in the Equal Error Rate of over 30% - providing improved security and improved privacy.

RNA editing is a regulatory event which sometimes occursa large number of procedures during the generation of RNA. During this editing, one or moreze that bases of the RNA strand is changed into an alternative base, possibly leading to changes in the splicing signals encoded RNA, the secondary structure of the RNA, or other changes. However, even though RNA editing is known to consistently change the resultant protein, the number of known editing sitesose is much smaller than what would be expected. Due to predict the existance of some RNA editing sites.

Existing models of facial personality trait judgments control a large number of likely sources of variance for real-world trait judgments. ! Our data set includes reliable trait ratings on images with these sources of variance included. ! It is possible to train a computer vision algorithm on trait-rated real-world images that approximates human trait judgments. ! This points to future avenues for empirically studying underexplored factors that contribute to personality judgments. Conclusions Question and Approach

Malicious network traffic, including widespread worm activity, is a growing threat to internet-connected networks and hosts. In this paper, we consider both syntax and semantics based approaches for dynamic network intrusion detection. The semantics-based approach can cope with sophisticated polymorphic and metamorphic worms better than the syntax-based approach. Our contribution in this work is threefold: our syntax-based scheme that uses variable-length partition with multiple breakmarks can detect many polymorphic worms; we believe our semantic-based prototype is the first NIDS that provides semantics-aware capability and our system is more efficient than what is reported by Christodorescu et al. (2005); our designed templates capture polymorphic shellcodes with added sequences of stack and mathematic operations.