As domotic technologies are evolving from home automation towards Ambient Intelligence, context aware adaptive solutions for advanced environment management are emerging, featuring a broad range of services customized on each user's specific needs. This scenario offers the opportunity to exploit the potential of face as a not intrusive biometric identifier not only to regulate access to the controlled environment but to adapt the ambient intelligence to the preferences of the recognized user. In this paper we present a 3D face recognition method applied to such an Ambient Intelligence framework. The proposed approach relies on stereoscopic face acquisition and 3D mesh reconstruction to avoid highly expensive and not automated 3D scanners, typically not suited for real time applications. For each subject enrolled, a bi-dimensional feature descriptor is extracted from its 3D mesh and compared to the previously stored correspondent template. This descriptor is a normal map, namely a color image in which RGB components represent the normals to the face geometry. A weighting mask, automatically generated for each authorized person, improves recognition robustness to a wide range of facial expression.

A biometric interface to ambient intelligence environments

RICCIARDI, STEFANO
2012-01-01

Abstract

As domotic technologies are evolving from home automation towards Ambient Intelligence, context aware adaptive solutions for advanced environment management are emerging, featuring a broad range of services customized on each user's specific needs. This scenario offers the opportunity to exploit the potential of face as a not intrusive biometric identifier not only to regulate access to the controlled environment but to adapt the ambient intelligence to the preferences of the recognized user. In this paper we present a 3D face recognition method applied to such an Ambient Intelligence framework. The proposed approach relies on stereoscopic face acquisition and 3D mesh reconstruction to avoid highly expensive and not automated 3D scanners, typically not suited for real time applications. For each subject enrolled, a bi-dimensional feature descriptor is extracted from its 3D mesh and compared to the previously stored correspondent template. This descriptor is a normal map, namely a color image in which RGB components represent the normals to the face geometry. A weighting mask, automatically generated for each authorized person, improves recognition robustness to a wide range of facial expression.
9783790827897
9783790827897
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11695/60269
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