Title: Estimation of centroid, ensembles, anomaly and association for the uniqueness of human footprint features

Authors: Kapil Kumar Nagwanshi; Sipi Dubey

Addresses: Department of Information Technology, MPSTME, Shirpur Campus, SVKM's NMIMS Mumbai, 425405, MS, India ' Department of CSE, Chhattisgarh Swami Vivekanand Technical University, Bhilai, 490020, CG, India

Abstract: Present study proposes an approach for the estimation of the distinctiveness of human footprints under the machine learning environment. In this system, a sum of 880 raw footprints have been segmented to get the 21 features for ensemble learning. All the features have been analysed for computation of minimum, mean grey value, median, maximum, standard deviation, kurtosis, and skewness for footprint dataset. The G-means clustering offers centroid information of footprint features. A set of 10 ensembles has analysed for surrogate footprint attributes. Ten anomaly models were created for anomaly scores among these features. The association of features gives the uniqueness of the human footprints for personal identification through fuzzy rules for every set of ensembles. As a consequence, centroid, ensembles, anomaly, and affiliation proved the individuality of human footprints.

Keywords: anomaly score; association; centroid; ensemble; footprint; G-means cluster; machine learning; recognition; rule-base; segmentation.

DOI: 10.1504/IJIEI.2020.109096

International Journal of Intelligent Engineering Informatics, 2020 Vol.8 No.2, pp.117 - 137

Accepted: 24 Feb 2020
Published online: 19 Aug 2020 *

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