|
Facial detection is a crucial stage in the facial recognition process. Misclassification during the facial detection process will impact recognitionresults. In this research, windowing system facial detection using the Gaborkernel filter and the fast Fourier transform was proposed. The training setimages, for both facial and non-facial images, were processed to obtain thelocal features by using the Gabor kernel filter and the fast Fourier transform.The local features were measured using probabilistic learning vectorquantization. In this process, facial and non-facial features were classifiedusing label 1 and -1. The proposed method was evaluated using facial and non-facial image testing sets, whichwere taken from the MIT+CMU image database. Thetesting images were enhanced first before the detection process using fourdifferent enhancement methods: histogram equalization, adaptive histogram equalization,contrast limited adaptive histogram equalization, and the single-scale retinexmethod. The detection results demonstrated that the highest average accuracywas 83.44%. |
