19036390
Audio-visual multimodal fusion for biometric person authentication and liveness verification
In this paper we propose a multimodal fusion framework based on novel face-voice fusion techniques for biometric person authentication and liveness verification. Checking liveness guards the system against spoof/replay attacks by ensuring that the biometric data is captured from an authorised live person. The proposed framework based on bi-modal feature fusion, cross-modal fusion as well as 3D shape and texture fusion techniques, allow a significant improvement in system performance against impostor attacks, type-1 replay attacks (still photo and prerecorded audio), and challenging type-2 replay attacks(CG animated video from a still photo and pre-recorded audio) and robustness to pose and illumination variations.
43139712
Speaker Verification with Fuzzy Fusion and Genetic Optimization
10506744
Fusion of Acoustic and Linguistic Features for Emotion Detection
This paper describes a system that deploys acoustic and linguistic information from speech in order to decide whether the utterance contains negative or non-negative meaning. An earlier version of this system was Passed QA to the Interspeech-2009 Emotion Challenge evaluation. The speech data consist of short utterances of the children’s speech, and the proposed system is designed to detect anger in each given chunk. Various frame-based cepstral, prosodic and acoustic features are extracted automatically and classified by means of a support vector machine. An automatic speech recognizer transcribes the utterances and yields a separate classification, based on the degree of emotional salience of the words. The emotionally salient words are computed on word hypotheses, so that un-transcribed training data is sufficient. Late fusion is applied to make a final decision on anger vs. non-anger of the utterance.
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.
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.
13438707
Interaction in a collaborative augmented reality environment
In this paper we describe an Augmented Reality (AR) system which allows multiple participants to interact with two- and three-dimensional data using tangible user interfaces. Interactively controllable 2D and 3D information is seamless integrated into the system.
13591397
A Context Query Language for Pervasive Computing Environments
This paper identifies requirements for querying and accessing context information in mobile and pervasive computing environments. Furthermore it studies existing query languages showing that they satisfy only a subset of these requirements or cover some of them only to a limited extent. A new context query language is presented to overcome these shortcomings, improving the state of the art in several respects: heterogeneous representations of context information, definition of complex filtering mechanisms, elaborate aggregation functions and ontology integration, all in one language.
9635742
An augmented virtuality approach to 3D videoconferencing
This paper describes the concept, prototypical implementation, and usability evaluation of an augmented virtuality (AV) based videoconferencing (VC) system: "cAR/PE!". We present a first solution which 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.
7724289
Outpatient clinic scheduling - a simulation approach
The process by which outpatients are scheduled for a doctor's visit is a crucial determinant of the overall efficiency of the patient flow. The problem at hand consists of determining prioritization (triage) rules so that adequate patient care is guaranteed, resources (provider schedules) are utilized efficiently and a service guarantee can be ensured. We present a simulation framework for the evaluation and optimization of scheduling rules. We outline the basic ingredients of our model, illustrate the kinds of analyses it has enabled us to perform and summarize our experience with a preliminary implementation for the Division of Pediatric Ophthalmology at Cincinnati Children's Hospital Medical Center. Challenges for adaptations to other settings are also outlined.
17740711
Hierarchical data access in structured P2P-networks
Hierarchically structured organizations have clear and relatively short communication paths. However, one does not take advantage of this fact in modern peer-to-peer overlays. We show how a command and control order can be imposed on a given peer-to-peer network. This allows fast hierarchical data storage and data access. Maintaining a hierarchical organization structure upon a given distributed hashtable overlay network scales with O(logN), where N is the number of peers in the network. This approach can be used to benefit from hierarchical structure in any communication system, especially in first response scenarios.