Keywords
Minutiae triplets
Triangle features
Corresponding triangle
Bayes’ theorem
Submitted : 2022-03-29
Accepted : 2022-04-13
Published : 2022-04-28
Abstract
In this study, two models for fingerprint and DNA identifications are constructed based on modern technologies, while offering significant advances over prior models. Our models have high credibility, obtaining relatively accurate results under different circumstances. Under different assumptions, this model tests the probability of the validity in the statement that human fingerprints are unique to be 93.94%. In other words, the percentage of misidentification is 6.06%. This model is a robust fingerprint identification method that can tolerate highly nonlinear deformations. The model is tested on the basis of a self-built database, proving that the model has high credibility, and convincing results are obtained from sensitivity analysis. In order to estimate the odds of misidentification by DNA evidence, we emphasized on two factors that might contribute to misidentification: random match probability and the probability arising from laboratory errors. Then, a model is developed using Bayes’ theorem to reveal the inherent relationship between them, which carries some reference value. The probability of matching by DNA evidence is estimated based on the changes in the significant level. Finally, the probabilities of misidentification by both fingerprint evidence and DNA evidence are compared using numerous data. We found that the probability of the former is 6.06% and that of the latter is smaller than 4.0 x 10?10. Therefore, it can be concluded that DNA identification is far better than that of fingerprint identification.
References
Derakhshani R, Schuckers SAC, Hornak LA, et al., 2003, Determination of Vitality from a Non-Invasive Biomedical Measurement for Use in Fingerprint Scanners. Pattern Recognition, 36(2): 383-396.
Tan X, Bhanu B, 2003, A Robust Two Step Approach for Fingerprint Identification. Pattern Recognition Letters, 24(13): 2127-2134.
Que C, Tan KB, Sagar VK, 2001, Pseudo-Outer Product Based Fuzzy Neural Network Fingerprint Verification System. Neural Netw, 14(3): 305-323.
Leis T, Repp R, Borkhardt T, et al.,1998, A New Fingerprint Method for Sequence Analysis of Chromosomal Translocations at the Genomic DNA Level. Leukemia, 12: 758-763.
Sujan VA, Mulqueen MP, 2002, Fingerprint Identification Using Space Invariant Transforms. Pattern Recognition Letters, 23(5): 609-619.
Ross A, Jain A, Reisman J, 2002, Proceedings of 16th International Conference on Pattern Recognition, August 11-15, 2002: A Hybrid Fingerprint Matcher. IEEE, Quebec City, QC, Canada, 795-598.
Rossa A, Jaina A, Reisman J, 2003, A Hybrid Fingerprint Matcher. Pattern Recognition, 36(7): 1661-1673.
Brodeur MB, Debruille JB, 2003, Visual Evoked Potentials to Line- and Luminance-Defined Triangles. Vision Research, 43(3): 299-306.
Tahmasebi AM, Kasaei S, 2002, A Novel Adaptive Approach to Fingerprint Enhancement Filter Design. Sig Proc: Image Comm, 17(10): 849-855.
Bhanu B, Tan X, 2004, Computational Algorithms for Fingerprint Recognition, Springer, Netherlands.
Dziuk P, 2003, Positive, Accurate Animal Identification. Reprod Sci, 79(3-4): 319-323.
Choubey D, Gutterman JU, 1996, The Interferon-Inducible Growth-Inhibitory P202 Protein: DNA Binding Properties and Identification of a DNA Binding Domain. Biochemical and Biophysical Research Communications, 221(2): 396-401.
Thompson WC, Taroni F, Aitken CGG, 2003, How the Probability of a False Positive Affects the Value of DNA Evidence. J Forensic Sci, 48(1): 47-54.
Taroni F, Biedermann A, Garbolino P, et al., 2004, A General Approach to Bayesian Networks for the Interpretation of Evidence. Forensic Sci Int, 139(1): 5-16.
Wan QH, Qian KX, Fang SG, 2003, A Simple DNA Extraction and Rapid Specific Identification Technique for Single Cells and Early Embryos of Two Breeds of Bos Taurus. Animal Reproduction Science, 77(1-2): 1-9.