Open AccessFace Recognition Use Local Image Dataset and Correlation Technique
Author(s) -
Dana Faiq
Publication year - 2021
Publication title -
uhd journal of science and technology
Language(s) - English
Resource type - Journals
eISSN - 2521-4217
pISSN - 2521-4209
DOI - 10.21928/uhdjst.v5n2y2021.pp26-31
Subject(s) - artificial intelligence , computer science , biometrics , facial recognition system , pattern recognition (psychology) , face (sociological concept) , feature extraction , three dimensional face recognition , computer vision , feature (linguistics) , wavelet , face detection , social science , linguistics , philosophy , sociology
Face recognition is an extreme topic in security field which identifies humans through physiological or behavioral biometric characteristics. Face recognition can also identify the human almost in a precise detection; one of the primary problems in face recognition is the accurate recognition rate. Local datasets use for implementing this research rather than using public datasets. Midian filter uses to remove noise and identify errors, also obtains a good accuracy rate without modifying image quality. In addition, filter processing applies to modify and progress images and the discrete wavelet transforms algorithm uses as feature extraction. Many steps are applied in this approach such as image acquisition, converting images into gray scale, cropping the image, and then passing to the feature extraction. In order to get the final decision about the indicated face, some required steps are used in the comparison. The results show the accuracy of 91% of the recognition rate through the human face.