Automated fingerprint processing is a mature art whose application rapidly is becoming more widespread. Fingerprint minutiae matching algorithms methodically compare minutiae points to determine whether two fingerprint templates match each other. Typically, these matching algorithms rely upon an analysis of the confidence of each individual minutia to improve performance or to reduce the computational complexity of the matching process by pruning out low confidence minutiae.
Prior art systems assign these minutiae confidence values by analyzing local and global properties of the image from which they were detected and extracted. These image properties are not tightly linked to the actual matching process and therefore do not always produce a reliable confidence value that predicts how likely a minutia point is to be matched in future verification attempts. It is the estimate of this likelihood that is most important when pruning or truncating the number of minutiae in a template.
Nothing in the prior art teaches assigning confidence values or pruning of individual templates.