Major improvements have been realised in the capture capability, enabling Iris capture on the move or from a distance. While this is not an improvement in the SDK matching per say, it has a significant influence on the matching and usability of the system.
There have been significant and drastic improvements in the quality and accuracy of matching performance in a very short period of time in the last few years. This has been demonstrated by recent NIST tests, as well as other independent testing. It is not anticipated this rate of improvement will level out any time soon; expect in the coming years further drastic improvements.
Accuracy is still continually improving, though not at the same drastic rate as face recognition, as this is a much older technology. However, areas where there are major improvements are in the automated processing of latent prints (both in automated ridge, minutiae identification, feature extraction, and in automated 10-print to latent matching). This has the potential to enable enhanced functionality at verification points, such as border crossings, by implementing functionality such as real-time watchlist checking against latent watchlists.
Multi-Biometric Record Level Fusion
Another area where developments are aiding in accuracy improvements is multi-biometric fusion, occurring at the record level. Rather than merging multiple candidate lists from multiple biometrics post search, fusing biometrics and biographics in-record has the potential to provide multi-biometric record-level scores. However, this has more of an impact in very large scale identification systems, as opposed to verification systems, or small scale databases, such as watchlist checking.
Biometric Matching as a Service
Supported by trends such as cloud computing, data center consolidation, shared infrastructure and virtualisation.
See here for more.