Open AccessLevenberg-marquardt backpropagation neural network with techebycheve moments for face detection
Author(s) -
Ali Nadhim Razzaq,
Rozaida Ghazali,
Nidhal K. El Abbadi,
Hussein Ali Hussein Al Naffakh
Publication year - 2021
Publication title -
bulletin of electrical engineering and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 12
ISSN - 2302-9285
DOI - 10.11591/eei.v10i5.2364
Subject(s) - backpropagation , artificial intelligence , artificial neural network , computer science , face (sociological concept) , pattern recognition (psychology) , levenberg–marquardt algorithm , feature extraction , feature (linguistics) , face detection , facial recognition system , computer vision , social science , linguistics , philosophy , sociology
Face detection is an intelligent approach used in a variety of applications that identifies human faces in digital images. This work presents a new method which composes of a neural network and Techebycheve transforms for face detection. For feature extraction, Tchebychev transform was applied, in which a discrete Tchebychev transform is given for different sampling patterns and several samples here were performed on color images. A Levenberg-Marquardt backpropagation neural network was applied to the transformed image to find faces in the face detection dataset and FDDB benchmarked database. Model performance was measured based on its accuracy and the best result from the newly proposed method was 98.9%. Simulation results showed that the proposed method handles face detection efficiently.