Facial detection is a crucial stage in the facial recognition process. Misclassification during the facial detection process will impact recognition
results. In this research, windowing system facial detection using the Gabor
kernel filter and the fast Fourier transform was proposed. The training set
images, for both facial and non-facial images, were processed to obtain the
local features by using the Gabor kernel filter and the fast Fourier transform.
The local features were measured using probabilistic learning vector
quantization. In this process, facial and non-facial features were classified
using label 1 and -1. The proposed method was evaluated using facial and non-facial image testing sets, which
were taken from the MIT+CMU image database. The
testing images were enhanced first before the detection process using four
different enhancement methods: histogram equalization, adaptive histogram equalization,
contrast limited adaptive histogram equalization, and the single-scale retinex
method. The detection results demonstrated that the highest average accuracy
was 83.44%.
