We attempt to index the similarity between two fingerprints by finding the correlation of their grayscale component values. The overall scheme is illustrated in the flowchart below. Because the grayscale color scheme gives an unjustified weighting to lighter pixels for a correlation calculation, we must normalize both input images before computing the correlation. This normalization will also ensure that the L2 inner product of the two images will have a value between 0 and 1, which is a convenient artifact for this type of matching.
Several factors influence our computation of the correlation.
First, we want to account for different translations, or shifts, of the fingerprint image. For example, if a print from one individual is in the upper left corner of the print area and a print from the same individual is located in the lower right corner, our system should identify the two prints as being highly correlated. Second, we need to consider computational complexity and the time it takes to perform this calculation. Because of these issues, we chose to implement a convolution based method of the convolution calculation. Convolution is an inner product calculation with various shifts.