Detail publikace

Contactless biometric hand geometry recognition using a low-cost 3D camera

Originální název

Contactless biometric hand geometry recognition using a low-cost 3D camera

Anglický název

Contactless biometric hand geometry recognition using a low-cost 3D camera

Jazyk

en

Originální abstrakt

In the past decade, the interest in using 3D data for biometric person authentication has increased significantly, propelled by the availability of affordable 3D sensors. The adoption of 3D features has been especially successful in face recognition applications, leading to several commercial 3D face recognition products. In other biometric modalities such as hand recognition, several studies have shown the potential advantage of using 3D geometric information, however, no commercial-grade systems are currently available. In this paper, we present a contactless 3D hand recognition system based on the novel Intel RealSense camera, the first mass-produced embeddable 3D sensor. The small form factor and low cost make this sensor especially appealing for commercial biometric applications, however, they come at the price of lower resolution compared to more expensive 3D scanners used in previous research. We analyze the robustness of several existing 2D and 3D features that can be extracted from the images captured by the RealSense camera and study the use of metric learning for their fusion.

Anglický abstrakt

In the past decade, the interest in using 3D data for biometric person authentication has increased significantly, propelled by the availability of affordable 3D sensors. The adoption of 3D features has been especially successful in face recognition applications, leading to several commercial 3D face recognition products. In other biometric modalities such as hand recognition, several studies have shown the potential advantage of using 3D geometric information, however, no commercial-grade systems are currently available. In this paper, we present a contactless 3D hand recognition system based on the novel Intel RealSense camera, the first mass-produced embeddable 3D sensor. The small form factor and low cost make this sensor especially appealing for commercial biometric applications, however, they come at the price of lower resolution compared to more expensive 3D scanners used in previous research. We analyze the robustness of several existing 2D and 3D features that can be extracted from the images captured by the RealSense camera and study the use of metric learning for their fusion.

BibTex


@inproceedings{BUT119834,
  author="Jan {Svoboda} and Michael {Bronstein} and Martin {Drahanský}",
  title="Contactless biometric hand geometry recognition using a low-cost 3D camera",
  annote="In the past decade, the interest in using 3D data for
biometric person authentication has increased significantly,
propelled by the availability of affordable 3D sensors. The
adoption of 3D features has been especially successful
in face recognition applications, leading to several commercial
3D face recognition products. In other biometric
modalities such as hand recognition, several studies have
shown the potential advantage of using 3D geometric information,
however, no commercial-grade systems are currently
available. In this paper, we present a contactless
3D hand recognition system based on the novel Intel RealSense
camera, the first mass-produced embeddable 3D
sensor. The small form factor and low cost make this sensor
especially appealing for commercial biometric applications,
however, they come at the price of lower resolution
compared to more expensive 3D scanners used in previous
research. We analyze the robustness of several existing 2D
and 3D features that can be extracted from the images captured
by the RealSense camera and study the use of metric
learning for their fusion.",
  address="IEEE Biometric Council",
  booktitle="Proceedings 2015 International Conference on Biometrics",
  chapter="119834",
  doi="10.1109/ICB.2015.7139109",
  edition="NEUVEDEN",
  howpublished="print",
  institution="IEEE Biometric Council",
  year="2015",
  month="may",
  pages="452--457",
  publisher="IEEE Biometric Council",
  type="conference paper"
}