MIRA : Moment Invariability Analysis of Footprint Features

Person authentication using footprint is still an abandoned field even though it has physiological and behavioral both types of available features due to unavailibilty of dataset. To examine the credibility of footprint we have collected the footprint dataset. This dataset collection is done in 2 phases. 1) We have collected the 2 footprint samples of each foot from 110 persons and 2) We have collected the 5 footprint sample of each foot from 80 people. The paper scanner is used for the data collection and whole footprint is captured. The collected samples are taken at different orientations and position, sometimes scanner is not aligned and creates noise.To overcome these problem a footprint image requires extensive preprocessing. To make any image invariant to translation and rotation, we use Hu's 7 moment invariant features. It can efficiently check that an input image belongs to a particular person or not even after translation, scaling and rotation. The probability of translation and scaling is very less in footprint, but slight rotation in foot image is noticeable, which could result in different geometry features for same person. This technique is not suitable for the authentication but it can surely reduce the sample space by rejecting the samples. If the difference of 3rd order moment invariant value of two samples is more then the decided threshold, then samples surely does not belong to the same person. This reduced sample size could be used further in authentication. It reduces the time complexity and computation cost. We tested it on 1320 images with the FMR of 4.52% and FNMR of 5.18%. It leads us to the conclusion that 3rd order of moment is enough to make any image rotation invariant.