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Journal ArticleDOI

Multimedia sensor fusion for retrieving identity in biometric access control systems

TLDR
A novel multimedia sensor fusion approach based on heterogeneous sensors for biometric access control applications uses multiple acoustic and visual sensors for extracting dominant biometric cues, and combines them with nondominant cues.
Abstract
In this article, we propose a novel multimedia sensor fusion approach based on heterogeneous sensors for biometric access control applications. The proposed fusion technique uses multiple acoustic and visual sensors for extracting dominant biometric cues, and combines them with nondominant cues. The performance evaluation of the proposed fusion protocol and a novel cascaded authentication approach using a 3D stereovision database shows a significant improvement in performance and robustness, with equal error rates of 42.9p (audio only), 32p (audio + 3D face + 2D lip features), 15p (audio + 3D face + 2D eye features), and 7.3p (audio-3D face + 2D lip + 2D eye-eyebrows) respectively.

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Citations
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Journal ArticleDOI

ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP)

Newton Lee
TL;DR: Call for papers for Special Issue of ACM Transactions on Multimedia Computing, Communications and Applications on Interactive Digital Television.
Journal ArticleDOI

Social Behavioral Information Fusion in Multimodal Biometrics

TL;DR: A novel person recognition approach is presented, that relies on the knowledge of individuals’ social behavior to enhance the performance of a traditional biometric system.
Patent

Electronic device with method for controlling access to same

TL;DR: In this paper, an electronic device is able to alter one or more settings of its imager based on the motion of a user that the device is attempting to authenticate, such as a video or still photo of the user.
Book ChapterDOI

Multimodal Identity Verification Based on Learning Face and Gait Cues

TL;DR: The experimental evaluation of the proposed scheme showed that the combined PCA-LDA face and gait features can lead to powerful identity verification and can capture the inherent multimodality in walking gait patterns and discriminate the identity from low resolution surveillance videos.
Proceedings ArticleDOI

Multimodal Face-Gait Fusion for Biometric Person Authentication

TL;DR: A novel multimodal fusion approach based on PCA-LDA processing for person identification from low resolution surveillance video with cues extracted from gait and face biometrics that can capture the inherent multimodality in walking gait patterns.
References
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Journal ArticleDOI

Independent component analysis: algorithms and applications

TL;DR: The basic theory and applications of ICA are presented, and the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible.
Journal ArticleDOI

An introduction to multisensor data fusion

TL;DR: This paper provides a tutorial on data fusion, introducing data fusion applications, process models, and identification of applicable techniques.
Journal ArticleDOI

A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition

TL;DR: This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images.
Book

Discrete-Time Speech Signal Processing: Principles and Practice

TL;DR: This chapter discusses the Discrete-Time Speech Signal Processing Framework, a model based on the FBS Method, and its applications in Speech Communication Pathway and Homomorphic Signal Processing.
Journal ArticleDOI

Recent advances in the automatic recognition of audiovisual speech

TL;DR: The main components of audiovisual automatic speech recognition (ASR) are reviewed and novel contributions in two main areas are presented: first, the visual front-end design, based on a cascade of linear image transforms of an appropriate video region of interest, and subsequently, audiovISual speech integration.
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