Open AccessFace geometry as a biometric-based identification system
Author(s) -
Catur Edi Widodo,
Kusworo Adi
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1524/1/012008
Subject(s) - biometrics , artificial intelligence , face (sociological concept) , computer vision , computer science , feature (linguistics) , feature extraction , hand geometry , pattern recognition (psychology) , centroid , facial recognition system , euclidean distance , identification (biology) , position (finance) , mathematics , social science , linguistics , philosophy , botany , finance , sociology , economics , biology
In this paper, we present a method for identifying a person with a biometric-based on face geometry. This person identification system design has been done using face biometrics by implementing feature extraction of distances between facial features. Face biometrics were chosen because they have unique characteristics and do not change in each person. In this method, a system analysis that separates the face image into face components, including the eyes, nose, and mouth on the face image is taken from the front view position. Each component of the feature that has been detected is measured by its distance to form the face semantics. The steps taken in image processing begin with determining the region of interest (ROI). Furthermore, the results of cropping ROI feature extraction are done to get the eye, nose and mouth. After that, a centroid is determined for each feature so that the distance between features can be calculated using the equation of the distance between the two coordinates, so we get 8 distances as matching parameters. The matching process is done using euclidean distance, which is calculating the smallest distance between the test image and the database. Based on research results, this system can be used to identify someone using face biometrics with fixed poses and expressions. The accuracy of this system is 100 percent