Levenberg-marquardt backpropagation neural network with techebycheve moments for face detection

Ali Nadhim Razzaq, Rozaida Ghazali, Nidhal Khdhair El Abbadi, Hussein Ali Hussein Al Naffakh


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.


Convolution neural network; Discrete tchebychev moments; Face detection; Image processing; Levenberg-marquardt backpropagation

Full Text:


DOI: https://doi.org/10.11591/eei.v10i5.2364


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Bulletin of EEI Stats