Statistical Assessment Of Fading Fingerprint

Abstract

Fingerprint emerged as an important system within the security agencies, government offices and court of law in the late 19th century, when it replaced anthropometric measurements as a more reliable method for identifying persons. No two fingerprints have ever been found identical in billions of human and automated computer comparisons. Fingerprint is thus claimed to outperform DNA and all other human identification systems. However, genetic mutation and effect of certain drugs have been found to influence changes in some of the fingerprint features but the extent has not been modeled adequately. This study is aimed at studying these potential differences from the effects of drugs. In the study, sample data in the form of patient’s fingerprints are transformed to quantitative data for statistical analysis. Two statistical approaches Gen Stats analysis and stochastic) are used. For the stochastic approach, we describe absolute changes in fingerprints as function of selected drugs and covariates patients’ age and duration of drug use. Fading fingerprint models for cancer chemotherapy are described as optimal control problems and the maximum level of toxicity store in the normal cells is represented by PT(Δx) = 1 - [1+ eβ0][1 + eβ0 – βΔx]-1 and this measures the swelling and expansion of the palm and consequently the peeling of ridges. We also discuss optimal therapies when the controls represent the effectiveness of chemotherapeutic agents, or, equivalently, when the simplifying assumption is that drugs act instantaneously. In addition to this, we describe the intensity of cancer with  wI(t) = 2rlr2Noq2,the level of damage done to DNA and PCR with ∫ 𝑡 0 w1(u)du = 2r1r2q2ƒN(u)udu where drug usage is zero using stochastic models, based on biological processes predicting future results in  fading fingerprint. We further established that the growth of cancer may be represented by x = (1-q)βs-(1-r)βr+√((1-q)βs-(1-r)βr)2+4rqβsβr> 0, where x is the ratio of sensitive killed-cells (S) to the resistant developed cells, R (that is, x = s/R). Thus, left alone, cancer cells grow exponentially reaching the relative proportions S = ẍ𝑅 ̅̅ ̅̅. This study has raised important medical issue of drug resistance and the maximum level of penalty in drug usage beyond the resistant stage. The effect of cancer drug model discussed here predicted the clinically established dandelion phenomenon and suggested depleting ridges by cancer drugs. The implication arising from the study suggests the need to avoid absolute reliance on fingerprint for identification and financial transactions. Consequently, it is recommended that a policy be put in place to monitor and review fingerprint features of cancer patients, and to incorporate other biometric characteristics (e.g. eye, gait) for purposes of identification.