16173188
Liveness Assurance in Face Authentication
The process of verifying whether the face image presented to an authentication system is real (i.e., alive), or whether it is reproduced or synthetic, and thus fraudulent. When a face authentication system is to recognize the face of a person by means of an electronic camera and associated image recognition software, it is important to be sure that the person seeking the authentication actually presents his or her face to the camera at the time and place of the authentication request. The face is presented live as on live television as distinct from a movie or cartoon programme. In contrast, an impostor could try to present a photograph, or a video recording, of a legitimate client to the camera in order to be falsely authenticated by the system as that client. That kind of threat to authentication systems is known generally as replay attack. In turn, liveness assurance uses a range of measures to reduce the vulnerability of face authentication systems to the threats of replay attack.
987011
Similarity normalization for speaker verification by fuzzy fusion
Abstract Similarity or likelihood normalization techniques are important for speaker verification systems as they help to alleviate the variations in the speech signals. In the conventional normalization, the a priori probabilities of the cohort speakers are assumed to be equal. From this standpoint, we apply the theory of fuzzy measure and fuzzy integral to combine the likelihood values of the cohort speakers in which the assumption of equal a priori probabilities is relaxed. This approach replaces the conventional normalization term by the fuzzy integral which acts as a non-linear fusion of the similarity measures of an utterance assigned to the cohort speakers. We illustrate the performance of the proposed approach by testing the speaker verification system with both the conventional and the fuzzy algorithms using the commercial speech corpus TI46. The results in terms of the equal error rates show that the speaker verification system using the fuzzy integral is more flexible and more favorable than the conventional normalization method.
25044094
Generalised Fuzzy Hidden Markov Models for Speech Recognition
A generalised fuzzy approach to statistical modelling techniques for speech recognition is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to hidden Markov models (HMMs). A more robust version of the above fuzzy technique based on the noise clustering (NC) method is also proposed. Experimental results were performed on the TI46 speech data corpus. A significant result for isolatedword recognition performed on a highly confusable vocabulary consisting of the nine English E-set words is that, a 33.8% recognition error rate for the HMM-based system was reduced to 30.5%, 29.9%, 29.8% and 27.8%, respectively, by using the FCM-HMM, the FE-HMM, the NC-FE-HMM, and the NC-FCM-HMM-based systems.
5705405
Robust face-voice based speaker identity verification using multilevel fusion
In this paper, we propose a robust multilevel fusion strategy involving cascaded multimodal fusion of audio-lip-face motion, correlation and depth features for biometric person authentication. The proposed approach combines the information from different audio-video based modules, namely: audio-lip motion module, audio-lip correlation module, 2D+3D motion-depth fusion module, and performs a hybrid cascaded fusion in an automatic, unsupervised and adaptive manner, by adapting to the local performance of each module. This is done by taking the output-score based reliability estimates (confidence measures) of each of the module into account. The module weightings are determined automatically such that the reliability measure of the combined scores is maximised. To test the robustness of the proposed approach, the audio and visual speech (mouth) modalities are degraded to emulate various levels of train/test mismatch; employing additive white Gaussian noise for the audio and JPEG compression for the video signals. The results show improved fusion performance for a range of tested levels of audio and video degradation, compared to the individual module performances. Experiments on a 3D stereovision database AVOZES show that, at severe levels of audio and video mismatch, the audio, mouth, 3D face, and tri-module (audio-lip motion, correlation and depth) fusion EERs were 42.9%, 32%, 15%, and 7.3%, respectively, for biometric person authentication task.
43139712
Speaker Verification with Fuzzy Fusion and Genetic Optimization
35465720
Reliability score based multimodal fusion for biometric person authentication
In this paper, we propose a robust multilevel fusion strategy involving cascaded fusion of hybrid multimodal fusion of audio-lip-face motion, correlation and depth features for biometric person authentication. The proposed approach combines the information from different audio-video based modules, namely: audio-lip motion module, audio-lip correlation module, 2D+3D motiondepth fusion module, and performs a hybrid cascaded fusion in an automatic, unsupervised and adaptive manner, by adapting to the local performance of each module. This is done by taking the output-score based reliability estimates (confidence measures) of each of the module into account. The module weightings are determined automatically such that the reliability measure of the combined scores is maximised. To test the robustness of the proposed approach, the audio and visual speech (mouth) modalities are degraded to emulate various levels of train/test mismatch; employing additive white Gaussian noise for the audio and JPEG compression for the video signals. The results show improved fusion performance for a range of tested levels of audio and video degradation, compared to the individual module performances. Experiments on a 3D stereovision database AVOZES show that, at severe levels of audio and video mismatch, the audio, mouth, 3D face, and tri-module (audio-lip motion, correlation and depth) fusion EERs were 42.9%, 32%, 15%, and 7.3% respectively for biometric person authentication task.
1173406
Geschäftsprozeßgestaltung durch Kommunikation und Information
32510848
Using Augmented Virtuality for Remote Collaboration
This paper describes the concept, prototypical implementation, and usability evaluation of the Augmented Virtuality (AV)-based videoconferencing (VC) system cAR/PE!. We present a solution that allows three participants at different locations to communicate over a network in an environment simulating a traditional face-to-face meeting. Integrated into the AV environment are live video streams of the participants spatially arranged around a virtual table, a large virtual presentation screen for 2D display and application sharing, and 3D geometry (models) within the room and on top of the table. We describe the general concept and application scenario as well as the actual hardware setup, the implementation, and the use of the system in its current state. Results of two usability studies with 87 subjects are presented that show the general usability of our approach as well as good overall satisfaction. Parts of the work described here were presented as a poster at the second International Symposium on Mixed and Augmented Reality (Regenbrecht, Ott, Wagner, Lum, Kohler, et al., 2003. An Augmented Virtuality Approach to 3D Videoconferencing. Poster at 2nd Int. Symp. on Mixed and Aug. Reality, Tokyo.).
2959784
Computational protein biomarker prediction: a case study for prostate cancer
BackgroundRecent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates.ResultsThorough cross-validation studies and randomization tests are performed on a prostate cancer dataset with over 300 patients, obtained at the Eastern Virginia Medical School using SELDI-TOF mass spectrometry. We obtain average classification accuracies of 87% on a four-group classification problem using a two-stage linear SVM-based procedure and just 13 peaks, with other methods performing comparably.ConclusionsModern feature selection and classification methods are powerful techniques for both the identification of biomarker candidates and the related problem of building predictive models from protein mass spectrometric profiles. Cross-validation and randomization are essential tools that must be performed carefully in order not to bias the results unfairly. However, only a biological validation and identification of the underlying proteins will ultimately confirm the actual value and power of any computational predictions.
42894925
Modeling of Context-Aware Self-Adaptive Applications in Ubiquitous and Service-Oriented Environments
Mobile computing in ubiquitous environments has to cope with both predictable and unpredictable changes in the execution context, which introduces the need for context-aware adaptive applications. Such environments are also characterized by dynamically discoverable services that can be utilized by applications to improve their functionality and quality of service (QoS). Thus, application adaptation decisions not only depend on context properties, but also on service availability and QoS-properties. In this chapter we present a novel comprehensive modeling approach that facilitates the model-driven development of such applications. Our focus is on modeling concepts which align the description of services and their QoS-properties with the context modeling approach. We provide a harmonized view on context and service properties, bridging the syntactical and semantic differences through an ontology. We also consider related aspects like semantic service discovery and service level agreements.
10411388
Large-scale linear programming techniques for the design of protein folding potentials
Abstract.We present large-scale optimization techniques to model the energy function that underlies the folding process of proteins. Linear Programming is used to identify parameters in the energy function model, the objective being that the model predict the structure of known proteins correctly. Such trained functions can then be used either for ab-initio prediction or for recognition of unknown structures. In order to obtain good energy models we need to be able to solve dense Linear Programming Problems with tens (possibly hundreds) of millions of constraints in a few hundred parameters, which we achieve by tailoring and parallelizing the interior-point code PCx.