Truth-value unconstrained face clustering for identity resolution in a distributed environment of criminal police information systems

The work of criminal police in contemporary society is characterized by the proliferation of data and information to be processed, a more significant limitation of access to personal data, increased public monitoring, and higher expectations in the efficiency of identifying perpetrators. Also, all information collected during investigations is distributed through police systems separated by different levels and organization units, and often there are insufficient human and material resources. Resolving the perpetrator’s identity in those circumstances is a complex task, and police decision support systems must group all available evidence related to specific persons. For that purpose, this paper proposes a new approach to unconstrained pairwise clustering of face feature vectors extracted from the histogram of oriented gradients descriptor, named Truth-value clustering (TVC), based on non-axiomatic logic and graphs. The clustering approach was experimentally tested with six different face image databases. They were created to simulate unconstrained conditions like IARPA Janus Benchmark-B Face Dataset (IJB-B), IMDb-Wiki, Labeled Faces in the Wild, and YouTube Faces. The results of the proposed solution are compared with other state-of-the-art methods, showing that the approach gives, in summary, significantly better results. Application of the IJB-B protocol created for testing face clustering showed that the new approach gives better results by an average of 8.25% (σ=4.2). The main advantage over other methods is the possibility of utilizing mechanisms from non-axiomatic logic such as revision, which can then acquire new knowledge based on information from different nodes of the distributed environment consisting of various police information systems.