One of the emerging applications of the millimeter-wave imaging technology is its use in biometric recognition.
This is mainly due to some properties of the millimeter-waves such as their ability to penetrate through clothing
and other occlusions, their low obtrusiveness when collecting the image and the fact that they are harmless to
health. In this work we first describe the generation of a database comprising 1200 synthetic images at 94 GHz
obtained from the body of 50 people. Then we extract a small set of distance-based features from each image
and select the best feature subsets for person recognition using the SFFS feature selection algorithm. Finally
these features are used in body geometry authentication obtaining promising results.
This paper reports for the first time experiments on the fusion of footsteps and face on an unsupervised and
not controlled environment for person authentication. Footstep recognition is a relatively new biometric based
on signals extracted from people walking over floor sensors. The idea of the fusion between footsteps and face
starts from the premise that in an area where footstep sensors are installed it is very simple to place a camera to
capture also the face of the person that walks over the sensors. This setup may find application in scenarios like
ambient assisted living, smart homes, eldercare, or security access. The paper reports a comparative assessment
of both biometrics using the same database and experimental protocols. In the experimental work we consider
two different applications: smart homes (small group of users with a large set of training data) and security access
(larger group of users with a small set of training data) obtaining results of 0.9% and 5.8% EER respectively for
the fusion of both modalities. This is a significant performance improvement compared with the results obtained
by the individual systems.