Application of Artificial Neural Networks in the Human Identification Based on Thermal Image of Hands

The aim of this study was to check the possibility of identifying the persons based on the properties of thermal maps and a temperature distribution of a hand, obtained from a thermal image, with use of artificial neural networks. For this purpose, a series of thermographs of the right hand of eight people was taken, with a thermal imaging camera. The photos were taken under the same thermal conditions, but with different state of warming of hands. After processing the photos (determining the edges, characteristic hand points and areas of interest), the parameters characterizing the metacarpal temperature distribution were determined. Eight parameters were chosen, which were average temperatures of the areas of interest. These parameters were input data of neural networks in the learning and identification process. As it was shown in this study, these parameters were sufficient to clearly identify the persons. Neural networks, designed as multi-layered perceptron, after proper learning showed very high values of identification parameters, including high values of sensitivity and specificity, what proves the high quality of classification. Such identification is possible with the natural thermal state of the hand and if thermal images are not strongly disturbed, the artificial neural networks are very good tool to implement in persons identification process.