Colored Petri Nets Construction for Predicting the Gender Classification Based on Human Body

Abstract

Today gender classification is an extremely difficult problem in a real-time application based on face recognition. Future trends will see a rise in demand for gender-based real-time applications. Some have suggested several methods for automatically classifying gender using physical characteristics of humans. We use data mining experiments using the proper gender classification dataset to conduct possible research. The first step is to extract the best applicable rules, and the second is to build a concurrent execution model, which is a Colored Petri net. The aim of this study is to find a high accuracy rule set for generating Fuzzy Petri nets and then implement it using a Colored Petri nets simulator.