A Fingerprint-based Access Control using Principal Component Analysis and Edge Detection
This work addresses access control security for fingerprint-based systems, but it appears incremental as it builds on existing techniques like PCA and edge detection without introducing a fundamentally new paradigm.
The paper tackles the problem of determining whether a fingerprint image belongs to a database by proposing a method that combines PCA and edge detection to create a training base and derive a new feature parameter H from distance relationships, resulting in improved decision-making as evidenced by lifting the ROC curve with thresholds set based on acceptable false positive and false negative rates.
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.