A Fingerprint-based Access Control using Principal Component Analysis and Edge Detection
This paper presents a novel approach for deciding on the appropriateness or not of an acquired fingerprint image into a given database. The process begins with the assembly of a training base in an image space constructed by combining Principal Component Analysis (PCA) and edge detection. Then, the parameter H, a new feature that helps in the decision making about the relevance of a fingerprint image in databases, is derived from a relationship between Euclidean and Mahalanobian distances. This procedure ends with the lifting of the curve of the Receiver Operating Characteristic (ROC), where the thresholds defined on the parameter H are chosen according to the acceptable rates of false positives and false negatives.
PDF Abstract
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
