학술논문
A partition based CNN approach in fingerprint identification for security and compared with KNN.
Document Type
Article
Author
Source
Subject
*HUMAN fingerprints
*CONVOLUTIONAL neural networks
*K-nearest neighbor classification
*SCHOOL enrollment
*STATISTICS
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Language
ISSN
0094-243X
Abstract
A comparative analysis of a novel convolutional neural network and k-nearest neighbors is performed to identify fingerprints in low-resolution photographs with high accuracy and sensitivity. Materials and methods: In this study, two groups were compared: a novel convolutional neural network (N=10) and a k-nearest neighbor algorithm (N = 10). Total sample size was estimated with an alpha of 0.05, an enrollment rate of 0.1, a confidence interval of 95%, and a power of 80% using G Power software. Results: The proposed method shows that the novel convolutional neural network achieves 93% accuracy and 90% sensitivity, while the K-Nearest Neighbor classifier achieves 89% accuracy and 87% sensitivity. The obtained accuracy rate was significant (p=0.005), and in the SPSS statistical analysis, the specificity value was (p=0.006). Conclusion: The new CNN classifier provides significantly better fingerprinting results than the K-Nearest Neighbors classifier. [ABSTRACT FROM AUTHOR]