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Öğe Evaluation of face recognition techniques using 2nd order derivative and new feature extraction method based on linear regression slope(Int Journal Computer Science & Network Security-Ijcsns, 2018) Alazzawi, Abdulbasit; Uçan, Osman Nuri; Bayat, OğuzFace 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.Öğe Neural network behavior analysis based on transfer functions MLP & RB in face recognition(Assoc Computing Machinery, 2018) Mohammed, Tareq Abed; Alazzawi, Abdulbasit; Uçan, Osman Nuri; Bayat, OğuzWe performed multi-layer perceptron neural networks MLPNN and Radial Basis neural networks RBNN. In the MLPNN, we applied three layers (input, Hidden, and output) with sigmoid transfer function. Similarly, we used RBNN. Both classifiers are used after preprocessing operations BIOID data set is used in training and testing phases to test the proposed face recognition system combinations. According to the experimental results, the proposed schemes achieved satisfactory results with high accuracy classificationÖğe Performance of face recognition system using gradient laplacian operators and new features extraction method based on linear regression slope(Hindawi Ltd, 2018) Alazzawi, Abdulbasit; Uçan, Osman Nuri; Bayat, OğuzRecent research proves that face recognition systems can achieve high-quality results even in non-ideal environments. Edge detection techniques and feature extraction methods are popular mechanisms used in face recognition systems. Edge detection can be used to construct the face map in the image efficiently, in which feature extraction techniques generate the most suitable features that can identify human faces. In this study, we present a new and efficient face recognition system that uses various gradient- and Laplacian-based operators with a new feature extraction method. Different edge detection operators are exploited to obtain the best image edges. The new and robust method based on the slope of the linear regression, called SLP, uses the estimated face lines in its feature extraction step. Artificial neural network (ANN) is used as a classifier. To determine the best scheme that gives the best performance, we test combinations of various techniques such as (Sobel filter (SF), SLP/principal component analysis (PCA), ANN), (Prewitt filter(PF), SLP/PCA, ANN), (Roberts filter (RF), SLP/PCA, ANN), (zero cross filter (ZF), SLP/PCA, ANN), (Laplacian of Gaussian filter (LG), SLP/PCA, ANN), and (Canny filter(CF), SLP/PCA, ANN). The BIO ID dataset is used in the training and testing phases for the proposed face recognition system combinations. Experimental results indicate that the proposed schemes achieve satisfactory results with high-accuracy classification. Notably, the combinations of (SF, SLP, ANN) and (ZF, SLP, ANN) gain the best results and outperform all the other algorithm combinations.Öğe Robust face recognition algorithm based on linear operators discrete wavelet transformation and simple linear regression(Assoc Computing Machinery, 2018) Alazzawi, Abdulbasit; Uçan, Osman Nuri; Bayat, OğuzFace recognition system performance would sharply decrease if there were noticeable issues in the face images conditions such as light variation, contrast, and brightness issues that can deeply affect the system performance directly. The process of face analysis comes here to put the face image environment in a spot of light to enable the interested researchers to find out the factors that have vital effects on these systems. In this paper, we are producing a hybrid method that based on discrete wavelet transformation DWT output and linear edge detection operators such as Sobel, Prewitt and Roberts output as a solution to cover some of these related image condition issues. For feature extraction, a new method based on simple linear regression slope with -SLP name-that proved the ability to find features in critical regions of the face, and eigenface based on principal components analysis PCA used with linear edge detection operators for comparison, studying the interrelation among them, and investigation the effects on the performance of the proposed system. A segmentation used to handle the details of a face image by dividing dataset images to equaled size blocks. Modified Artificial neural network MANN used for classification and all results obtained evaluated. The proposed method examined and evaluated with different face datasets using modified MANN classifier. The experimental results were displaying the superiority of the proposed algorithm over the algorithms that used the state-of-art techniques.
