DEVELOPMENT OF A METHOD FOR PERSONAL IDENTIFICATION BASED ON THE PATTERN OF PALM VEINS
Abstract
The article describes the work on the creation of a neural network method for identifying
a person based on the mechanism of scanning and analyzing the pattern of palm veins as a biometric
parameter. As part of the study, the prerequisites, goals and reasons for which the deve lopment
of a reliable biometric identification system is an important and relevant area of activity
are described. A number of problems are formulated that are inherent in existing methods for
solving the problem: the graph method and the method based on calculating the distance expressed
in various interval metrics. The description of the principles of their work is given.
The tasks solved by personal identification systems are formulated: comparison of the subject of
identification with its identifier, which uniquely identifies this subject in the information system.
A mechanism for reading a pattern of veins from the palm of the hand, developed for analyzing
an image obtained with a digital camera sensitive to infrared radiation, is described. When the
palm is in the frame, illuminated by the light of the near infrared range, the image obtained
from the camera becomes noticeable pattern of veins, vessels and capillaries that lie under the
skin. Depending on the organization, the identification system may, based on the provided identifier,
determine the appropriate access subject or verify that the same identifier belongs to the
intended subject. Three methods for further analysis of biometric data and personal identification
are given: approaches based on categorical classification and binary classification, as well
as a combined approach, in which identification is first used by the first method, and then, by
the second, but already for a known access identifier defined on the first stage. The resulting
architecture of the neural network for the categorical classification of the vein pattern is pr esented,
a method for calculating the number of model parameters depending on the number of
registered subjects is described. The main conclusions and experimental measurements of the
accuracy of the system when implementing various methods are presented, as well as diagrams of
changes in the accuracy of models during training. The main advantages and disadvantages of the
above methods are revealed.
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