Fingerprint Recognition under Missing Image Pixels Scenario
This addresses fingerprint recognition reliability in scenarios with incomplete data, but it is incremental as it applies an existing method to a specific problem.
The paper tackled fingerprint recognition when images have missing pixels by using Compressive Sensing for reconstruction, achieving successful person identification even with less than 90% of pixels missing.
This work observed the problem of fingerprint image recognition in the case of missing pixels from the original image. The possibility of missing pixels recovery is tested by applying the Compressive Sensing approach. Namely, different percentage of missing pixels is observed and the image reconstruction is done by applying commonly used approach for sparse image reconstruction. The theory is verified by experiments, showing successful image reconstruction and later person identification even if less then 90% of the image pixels is missing.