Conference Paper

As face recognition solutions based on artificial intelligence are becoming popular, it is critical to fully understand and explain how these technologies work in order to make them more effective and accepted by society. This article presents XAIface, a CHIST-ERA project that focuses on the analysis of the influencing factors relevant for the final decision of AI-based face recognition systems as an essential step to understand and improve the underlying processes involved. The scientific approach pursued in the project is designed in such a way that it will be applicable to other use cases such as object detection and pattern recognition tasks in a wider set of applications. The achieved results will feed into the implementation of an end-to-end face recognition system for studying the impact of the various system processes in terms of recognition performance and explainability.