A multi-sensor network for the protection of cultural heritage

The paper presents a novel automatic early warning system to remotely monitor areas of archaeological and cultural interest from the risk of fire. Since these areas have been treasured and tended for very long periods of time, they are usually surrounded by old and valuable vegetation or situated close to forest regions, which exposes them to an increased risk of fire. The proposed system takes advantage of recent advances in multi-sensor surveillance technologies, using optical and infrared cameras, wireless sensor networks capable of monitoring different modalities (e.g. temperature and humidity) as well as local weather stations on the deployment site. The signals collected from these sensors are transmitted to a monitoring centre, which employs intelligent computer vision and pattern recognition algorithms as well as data fusion techniques to automatically analyze sensor information. The system is capable of generating automatic warning signals for local authorities whenever a dangerous situation arises, as well as estimating the propagation of the fire based on the fuel model of the area and other important parameters such as wind speed, slope, and aspect of the ground surface. © 2011 EURASIP

Use of depth and colour eigenfaces for face recognition

In the present paper a face recognition technique is developed based on depth and colour information. The main objective of the paper is to evaluate three different approaches (colour, depth, combination of colour and depth) for face recognition and quantify the contribution of depth. The proposed face recognition technique is based on the implementation of the principal component analysis algorithm and the extraction of depth and colour eigenfaces. Experimental results show significant gains attained with the addition of depth information

A face and gesture recognition system based on an active stereo sensor

The paper presents several novel 3D image analysis algorithms, applied towards the segmentation and modeling of faces and hands. These are subsequently used to build a face-based authentication system and a system for humancomputer interaction based on static and dynamic gestures. The system relies on an active stereo sensor that uses a structured light approach to obtain 3D information. In this paper we demonstrate how the use of 3D information may significantly improve the efficiency of traditional face and gesture recognition techniques that use 2D images only. 1

Rank-based Decision Fusion for 3D Shape-based Face Recognition

Abstract. In 3D face recognition systems, 3D facial shape information plays an important role. Various shape representations have been proposed in the literature. The most popular techniques are based on point clouds, surface normals, facial profiles, and statistical analysis of depth images. The contribution of the presented work can be divided into two parts: In the first part, we have developed face classifiers which use these popular techniques. A comprehensive comparison of these representation methods are given using 3D RMA dataset. Experimental results show that the linear discriminant analysis-based representation of depth images and point cloud representation perform best. In the second part of the paper, two different multiple-classifier architectures are developed to fuse individual shape-based face recognizers in parallel and hierarchical fashions at the decision level. It is shown that a significant performance improvement is possible when using rank-based decision fusion in ensemble methods.