Face Detection in Image Frames and Matching Through Face Database

In this study, feature extraction is performed using Gabor wavelet transforms and Dual Tree wavelet transforms for face detection. Artificial neural networks with feed forward are used in the classification step. In the first of the proposed algorithms, the Dual Tree feature extraction vectors are used to train the neural networks, while in the second proposed algorithm, the Gabor feature extraction vectors are used in the neural network training. The proposed third algorithm consists of combining the perception results of the first two algorithms with OR logic operation. The performance calculation of the system is realized with three metrics in which the wrong perception rate is included in the account. Simulations were performed on MIT + CMU, FRAV2D, BioID, BANCA databases. The dimensions of the Gabor wavelet vectors are reduced to different ratios and the effects on the processing time and performance are examined