General borda count for multi-biometric retrieval

Indexing of multi-biometric data is required to facilitate fast search in large-scale biometric systems. Previous works addressing this issue were challenged by including biometric sources of different nature, utilizing the knowledge about the biometric sources, and optimizing and tuning the retrieval performance. This work presents a generalized multi-biometric retrieval approach that adapts the Borda count algorithm within an optimizable structure. The approach was tested on a database of 10k reference and probe instances of the left and the right irises. The experiments and comparisons to five baseline solutions proved to achieve advances in terms of general indexing performance, tunability to certain operating points, and response to missing data. A clear advantage of the proposed solution was noticed when faced by candidate lists of low quality.