A Biometric Fusion System of Face and Fingerprint for Enhanced Human Identification Using HOG-LBP Approach
This paper presents a biometric fusion system of fingerprint and face images for Ergonomic-Based Enrolment and
Verification System. Features from fingerprints and faces are extracted to create a new biometric template with
enhanced performance and with an extra level of assurance for identification. A fusion scheme combines the
extracted Histogram of gradients (HOG) and local Binary Pattern (LBP) features from a subject’s fingerprint and face
images. Manhattan Distance is used to compare between the template in the database and the input data. The
difference between the database template and the input data determines the decision either to reject or accept.
Different "matching score thresholds" were set to evaluate the relationship between False Rejection Ratio and False
Acceptance Ratio which is a common measure to determine system performance level. From the experiments and
based on the characteristic nature of this HOG-LBP algorithm, a threshold between 75% and 80% is determined to
be moderate and close to the EER (Equal Error Rate) point, which is the intersection of the False Accept Rate (FAR)
and False Reject Rate (FRR). The system is robust enough to accommodate an increase in the threshold if a high level
of system confidence is required
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