Gabor wavelet and neural network face detection

Department of Anesthesia Techniques, College of Medical and Health Techniques, University of Alkafeel, Najaf, 54001, Iraq

^* Corresponding Author: yasir.alaabedi@alkafeel.edu.iq

Abstract

One of the most difficult tasks in image processing is facial area detection. This study introduces a new face detection method. To improve detection rates, the system incorporates two facial detection algorithms. Gabor wavelets and neural networks are the two algorithms. Convolutional face images undergo initial transformation using Gabor wavelets, with 8 orientations and 5 scales chosen to extract the grey characteristics of the facial region. When added to the original photos, these 40 Gabor wavelets reveal the full extent of the response. We use a second feedforward neural network specifically designed for facial detection. The neural network is trained by backpropagation using the training set of faces and non-faces. Our experiments show that the suggested Gabor wavelet faces, when combined with the neural network feature space classifier, provide very respectable results. Comparing our proposed system to other face detection systems reveals that it performs better in terms of detection and false negative rates.