Intruder Identification Using Footprint Recognition with PCA and SVM Classifiers

Abstract

In modern digital era authentication has been done using biometric recognition. This biometric includes finger print, footprint, facial recognition, DNA of a person’s gene, hand palm print and eye’s iris recognition. The widely used among these is finger print and iris recognition. In this work we proposed a biometric recognition using footprints of a person. Earlier work deals with capturing footprint on a paper or on a surface. This won’t give us accurate foot print, since it depends on nature of the surface, quality of the paper and proper placement of the foot to give good foot print impression. To avoid all these we proposed a touch less method to obtain foot prints. The footprint can be obtained using any digital camera. We can take footprint image in many angles to conform the individuality of a person. In this work we used Principle Component Analysis (PCA) for pattern recognition and feature extraction. Then the SVM classifier split the patterns in to relevant classes. In early stage of our work itself we got remarkable quality and it is comparatively better than conventional footprint images obtained using paper or surface