61094772
Video to the rescue
Automatic person identity verification based on biometrics is a challenging problem, and has received much attention during recent years due to its many applications in on-line transaction processing, law enforcement, and security applications. However, most identity verification systems are primarily based on voice biometrics, and hence are more vulnerable to acoustic noise and channel distortions, in addition to train/test mismatch conditions. In this paper, we show how we can use video information to improve the performance of identity verification systems. The approach based on multimodal fusion of voice and face information from speaking face video allows robust identity verification performance. In addition, depending on the type of features and fusion technique used, it is also possible to perform liveness checks, allowing the system to detect fraudulent attacks on the system.
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.
43139712
Speaker Verification with Fuzzy Fusion and Genetic Optimization
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.
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.
13925823
Context inference of users' social relationships and distributed policy management
Inference of high-level context is becoming crucial in development of context-aware applications. An example is social context inference - i.e., deriving social relations based upon the user's daily communication with other people. The efficiency of this mechanism mainly depends on the method(s) used to draw inferences based on existing evidence and sample information, such as a training data. Our approach uses rule-based data mining, Bayesian network inference, and user feedback to compute the probabilities of another user being in the specific social relationship with a user whose daily communication is logged by a mobile phone. In addition, a privacy mechanism is required to ensure the user's personal integrity and privacy when sharing this user's sensitive context data. Therefore, the derived social relations are used to define a user's policies for context access control, which grant the restricted context information scope depending on the user's current context. Finally, we propose a distributed architecture capable of managing this context information based upon these context access policies.
578051
Bayesian Biosurveillance of Disease Outbreaks
Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be performed in real time as population data stream in. We describe techniques we have applied to address both the modeling and inference challenges. A key contribution of this paper is the explication of assumptions and techniques that are sufficient to allow the scaling of Bayesian network modeling and inference to millions of nodes for real-time surveillance applications. The results reported here provide a proof-of-concept that Bayesian networks can serve as the foundation of a system that effectively performs Bayesian biosurveillance of disease outbreaks.
751960
Performance Evaluation Methodologies for Abstract Data Type Implementation Techniques
Abstract The runtime management system for a programming language must allow the efficient manipulation of complex objects, such as instances of abstract data types (ADTs). We describe an implementation model for ADTs based on coroutine objects (i.e. as an alternative to ad hoc modifications of the runtime stack). We evaluate the performances of variants of our implementation model using two different approaches: a benchmarking technique which monitors actual costs for test programs and a “pseudo-execution” technique which estimates costs based on idealized instruction timings. The results of our comparison indicate that the two forms of evaluation give very similar cost estimates, supporting the use of the (cheaper) pseudo-execution technique. The overall performance results identify applications in which our coroutine implementation model has definite strengths and weaknesses. Finally, our implementation approach is compared with modified stack implementations used in other ADT systems.
209498655
Preface
The title of this book ‘One-Dimensional Finite Elements: An Introduction to the FE Method’ stands for content and focus. Nowadays, much literature regarding the topic of the finite element method exists. The different works reflect the multifaceted perceptions and application possibilities. The basic idea of this introduction into the finite element method relies on the concept of explaining the complex method with the help of one-dimensional elements. It is the goal to introduce the manifold aspects of the finite element method and to enable the reader to get a methodical understanding of important subject areas. The reader learns to understand the assumptions and derivations at different physical problems in structural mechanics. Furthermore, he/she learns to critically evaluate possibilities and limitations of the finite element method. Additional comprehensive mathematical descriptions, which solely result from advanced illustrations for twoor three-dimensional problems, are omitted. Hence, the mathematical description largely remains simple and clear. The focus on one-dimensional elements, however, is not just a pure limitation on a simpler and clearer formal illustration of the necessary equations. Within structural engineering, there are various structures— for example bridges or high transmission towers—which are usually modeled via one-dimensional elements. Therefore, this work also contains a ‘set of tools’, which can also be applied in practice. The concentration on one-dimensional elements is new for a textbook and allows the treatment of various basic and demanding physical questions of structural mechanics within one single textbook. This new concept, therefore, allows a methodical understanding of important subject areas (for example plasticity or composite materials), which occur to a prospective engineer during professional work, which however are seldom treated in this way at universities. Consequently, simple access is possible, also in supplementary areas of application of the finite element method. This book originates from a collection of lecture notes which were developed as written material for lectures and training documents for specialized courses on the finite element method. Especially, the calculated examples and the supplementary
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.