Data Fusion based Two-stage Cascade Framework for Multi-Modality Face Anti-Spoofing

Anti-spoofing, Face

A Two-Stage Approach to Multi-Sensor Temporal Data Fusion

This paper proposes a two-stage architecture for multi-sensor temporal data fusion. The first stage uses extended Kalman filters to track tokens seen by each sensor, and the second stage links the tokens corresponding to the same real-world event. Two pairs of strategies are presented relating to the initial data association between tokens and filters, together with decision rules for switching...

A Distributed Face Recognition Framework Based on Data Fusion

To accomplish face recognition more efficiently, a distributed face recognition framework based on MB-LBP features and data fusion is presented in this paper. Firstly, four face regions are interactively marked and the Multi-scale Block Local Binary Patterns are extracted from these regions to achieve both locally and globally informative features. Secondly, a distributed framework is introduce...

Opportunities for Data Fusion in Multi-Modality Tomography

It is now possible to fit several tomographic systems to an industrial process. By the use of different modalities to image the process, the process engineer can plan to take advantage of the strongest features provided by each unit. Detailed information from different modalities might be gained by inspection of separate tomograms. To maximise the information available from the combined tomogra...

Spatiotemporal Fusion Framework for Multi-camera Face Orientation Analysis

In this paper, we propose a collaborative technique for face orientation estimation in smart camera networks. The proposed spatiotemporal feature fusion analysis is based on active collaboration between the cameras in data fusion and decision making using features extracted by each camera. First, a head strip mapping method is proposed based on a Markov model and a Viterbi-like algorithm to est...