Human Identification with their VOC Distribution Through CMS – SEN Model

Abstract Smell printing or odor printing is a novel morphological characteristic that an object can be defined by its odor. Human body odor is one such biological trait that yields less error rate of 15% among other biometrics. The human odor printing or smell printing possesses significance against the world towards screening of security checkpoint, searching for survivals under rubbles, investigating criminals, and many more. Cogno-monitoring system (CMS) is a specific prototype to furnish two essential processes -odor analysis and odor encoding through the Sensing-Encoding-Notifying (SEN) model to give the sensitivity and specificity score among the individuals. Human body odor can be interpreted as the alliance of various volatile organic compounds (VOCs) and they are recognized, classified in the encoding process. This article exhibits a detailed analysis of the traditional detection methods including bioanalysis concerning the human body human body odor experimented with 6 people. By applying principal component analysis along with random forest classifier, the VOCs distribution of the individuals is measured. This work calculates that 18.7% of VOCs are having a match with all the individuals which become the plinth for the identification of humans.