gb2s
-->

Hand

hand
 What is hand geometry?
 

Hand biometric systems have evolved from early approaches which considered flat-surface and pegs to guide the placement of the user’s hand, to completely platform-free, non-contact techniques were user collaboration is almost not required.

At present, trends in biometrics are inclined to provided human identification and verification without requiring any contact with acquisition devices. The point of aiming contact-less approaches for biometrics regards the upward concerns with hygiene and final user acceptability.

 

 How does hand analysis work?

 

Hand geometry involves the following steps:

  1. Hand Image Acquisition and Pre-Processing. Contactless biometrics impose on users almost no constraints in terms of distance to camera, hand orientation and so forth. The pre-processing method contains several steps, briefly described as follows: 1) Segmentation, which consists of isolating hand from background precisely. 2) Finger classification, carried out after segmentation process, it consists of identifying each finger (index, middle, ring or little) correctly with independence of previous possible changes (rotation, hand orientation, pose and distance to camera). 3) Valleys and tips detection, essential in order to provide accurate mark points from which features can be extracted. 4) Left-Right hand classification, based on the fact that an individual can provide any hand, and the system must firstly classify the hand. Notice that without this method, fingers from left hand could be compared to fingers from right hand, resulting in errors in identification.

  2. Feature Extraction. The proposed method extracts features by dividing the finger from the basis to the tip in m parts. Each of these former parts measures the width of fingers, based on the Euclidean distance between two pixels. Afterwards, for each finger, the m components are reduced to n elements.

  3. Template Definition. Creation of the hand template considering only samples (hand feature vectors) from a single individual is done.

  4. Matching Based on the Hand Distances Template. Provided the template, which collects global information from samples of a same individual, it is mandatory the definition of a likelihood function able to indicate to what extent an acquire sample (impostor or genuine) is similar to previous template.

 

  Applications

 

  • Secure systems based on authentication (verification) in mobile devices in order to avoid spoofing attacks.
  • Security in payment on mobile devices.
  • Acquisition of transport tickets in different platforms: underground, train, bus, plane. Save in paper and dedicated devices.
  • Blocking mobile. Limited access to certain applications in the mobile (mail, agenda).
  • Security, alarm management from the mobile device.
  • Bounded to a buying ticket (access to a specific event associated to the service the user has acquired).
  • Adecuate age confirmation for visualization in mobile devices.

 

  Our Projects

 

 

Category: /

Grup of Biometrics, Biosignals and Security (GB2S)