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Evaluation of Face Recognition Techniques Using 2nd Order Derivative and New Feature Extraction Method Based on Linear Regression Slope


Abdulbasit Alazzawi, Osman N. Ucan, Oguz Bayat


Vol. 18  No. 3  pp. 169-177


Face recognition system has been widely utilized for various sensitive applications such as Airport gates, special monitoring, and tracking system. The performance of most face recognition systems would significantly decrease if there were several variations in the illumination of dataset images. In this paper the proposed a new algorithm based on a combination of edge detection operators, features extractors and artificial neural network ANN as a classifier. The Second based on Laplacian comprise Zero cross, Laplacian of gaussian LOG, and Canny edge detection filters. A segmentation process is used to segment each image to equaled size blocks treats face edge pixels precisely. A new features extractor technique based on Linear Regression Slope SLP with discrete wavelet transformation (DWT) and principle components analysis PCA used for features extraction. ANN used for the data set classification and all results obtained evaluated. We tried a combination of various techniques like (Zero cross, DWT, SLP-PCA, ANN),(LOG, DWT, SLP-PCA, ANN),(Canny, DWT, SLP-PCA, ANN ). The proposed method is examined and evaluated with different face datasets using ANN classifier. The experimental results were displaying the superiority of the proposed algorithm over the algorithms that used the state-of-art techniques where the combinations (Zero cross, SLP, ANN) gained the best results and could outperform all the other algorithms.


Face Recognition, SLP, PCA, Neural Network, ANN