A multi-attribute decision-making system for facial recognition software evaluation using complex picture fuzzy soft sets

Technological advancements and artificial intelligence raise security concerns, with facial recognition being a particularly effective security measure. Choosing the best facial recognition software involves a comprehensive decision-making approach due to its sensitivity and potential ambiguities, ensuring the highest level of security. The main aim of the research is to introduce a new theoretical framework called the complex picture fuzzy soft set (CPFS-set), which manages information-based uncertainty, periodicity, and vagueness by combining the flexibility of soft sets and complex picture fuzzy sets. Basic concepts, such as types and set operations, are examined mathematically with numerical examples to clarify the ideas. An intelligent decision-support mechanism is developed using a robust algorithm to illustrate the construction and computation stages of facial recognition software evaluation with the CPFS-set. This comprehensive analysis helps stakeholders make informed decisions based on empirical data and rigorous evaluation.