Current fingerprint matching algorithms are sensor dependent, and would work fine with the corresponding sensor for which the algorithm was developed. For example, an algorithm that was developed to work with optical sensors would perform badly when working with images obtained from a less accurate solid state sensor. There are many kinds of sensors from which a fingerprint image could be obtained, but still no one algorithm that can work with all these sensors. In the fingerprint verification competition that was held in 2004, (FVC2004), this was the theme, and many algorithms were found to be sensor dependent. And the need for an algorithm of this sort, that can work with multiple sensors was cited by Prof.Anil K Jain through a paper in 2004, titled "Sensor Interoperability". So, the research that is going on now is pertaining towards building an algorithm that would work with images obtained from any kind of sensor. The desirable output of the algorithm would be to authenticate a person by matching an input image (from one kind of a sensor; say a solid state sensor) with a base image that was obtained from a different sensor. [Base image is already obtained during enrollment; while the input image is acquired for either verification or identification]. The idea that is being used is as follows: the inter Euclidean distances in terms of pixels between a pair of minutiae points, like two bifurcations or two ridge endings is expressed as a ratio, with respect to a formula explained in the algorithm. This ratio is checked in the input image, after the minutiae points are initially identified. The algorithm works on the binary format of the fingerprint image. Other prematching steps like converting the image into a binary image, and thinning are done prior to starting the matching module.