Open AccessImproving the accuracy of old and young face detection in the template matching method with Fuzzy Associative Memory (FAM)
Author(s) -
Rafika Sari Br Sembiring,
Syahril Efendi,
Saib Suwilo
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/725/1/012117
Subject(s) - optimal distinctiveness theory , face detection , artificial intelligence , computer science , face (sociological concept) , object class detection , pattern recognition (psychology) , template matching , biometrics , computer vision , matching (statistics) , three dimensional face recognition , facial recognition system , psychology , mathematics , image (mathematics) , social science , statistics , sociology , psychotherapist
Every human has a face pattern and certain characteristics even though identical twins, but the human face pattern still has its own distinctiveness as well as old face patterns and young face patterns even though the human face pattern is very diverse but for young and old face patterns will be a difference between one face and the other face. Face detection (face detection) is one of the initial stages that very important in face recognition that is used in biometric identification. Face detection can also be used to search or index face data from images or videos that contain faces of various sizes, positions, and backgrounds. Face detection (face detection) automatically with the help of a computer is a problem that is not easy because the human face has a high level of variability both intra-personal and extra-personal variability. This study shows that systems with template matching methods combined with FAM can successfully detect differences in human faces, 80% accuracy, 10% better by using ordinary template matching.