Novel Distance Metric for Touch less Footprint Based Identification Technique
Anshu Gupta1, Deepa Raj2
1Anshu Gupta, pursuing Ph.D. Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow.
2Deepa Raj, Assistant Professor, Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow.
Manuscript received on December 20, 2019. | Revised Manuscript received on December 28, 2019. | Manuscript published on January 10, 2020. | PP: 1011-1016 | Volume-9 Issue-3, January 2020. | Retrieval Number: C7967019320/2020©BEIESP | DOI: 10.35940/ijitee.C7967.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this fast-paced technology-driven today’s era, biometrics is not the new buzzword in the information security domain. Biometrics uses any physiological or/ and behavioral attribute/s of an individual for personal identification and/or verification. In biometrics, so many traits, like a fingerprint, face, palm, retina, iris, ECG, gait, voice, and signature, etc., have been used from ages to uniquely identify a human being. Biometrics based on Footprints is the latest practice for personal identification. Like fingerprints and palmprints, footprints of individuals carry uniqueness; hence can be used in biometrics for personal recognition. This work investigates the powerfulness of footprints by extracting texture and shape features using Principal Component Analysis (PCA) method based upon Eigenfeet and introduces a new distance metric during the matching phase. Experimental results show that the new distance metric shows better results in comparison to the Euclidean, Manhattan and Mahalanobis distances.
Keywords: Biometrics, Footprint Recognition, Eigenfeet, PCA, Distance metrics
Scope of the Article: Pattern Recognition