Uncertain -Z Numbers based Face Sketch Recognition

This research explores the fusion of fuzzy constraints and reliability to model human behaviors, referred to as Z-Numbers. However, Z-Numbers rely on specific information representation concepts, whereas it is widely acknowledged that interval concepts align better with our daily experiences. This motivation drives us to employ an enhanced version of ZNumbers to address real-world challenges, such as the Sketching with Words (SWW) problem. SWW aims to automate the ranking of criminal face sketches in descending order of similarity, given that observer statements are fraught with perceptual uncertainties and fuzzy linguistic terms. Consequently, a dependable tool is imperative for practical problem-solving. The utilization of interval-valued complex fuzzy sets, centered on uncertain Z-Numbers, emerges as a promising approach to address such perception-based multicriteria group decision-making issues. In our proposed work, we incorporate uncertain Z-Numbers within the framework of interval-based fuzzy constraints and reliability. This approach aims to minimize the chances of criminals evading justice while safeguarding the rights of the innocent