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Öğe Palmprint recognition system based on gabor wavelet transform with K-NN classifier model(INESEC, 2017) Çalışkan, Abidin; Ergen, Burhan; Acar, EmrullahPalmprint recognition system is regarded as reliable and accurate biometric identification system. The biometric approach palm recognition system has attracted the attention of researchers in recent times because of the presenting a new biometric feature compared to other models. In this work, gabor wavelet transform (GWT) based palmprint recognition system has been developed. Firstly, image coordinate system is determined in order to facilitate image alignment for feature extraction. Then, region of interest is cropped from the palmprint images. With the developed system, features are extracted from the region of interest and they are given to k-nearest neighbors (k-NN) classifier as input parameters. Finally, the highest success rate for GWT based systematic sampling was computed as 86.90% according to the non-request data selection and it was observed that the proposed recognition system provide successful results in classification of palmprint images. Moreover, a good identification of the feature vector is the main factor that affects performance. Thus, the performance can also be improved by finding more suitable feature vectors.Öğe Fingerprint recognition system based on gray level co-occurrence matrix(INESEC, 2017) Çalışkan, AbidinThe biometric system provides an automatic identification of any person, depending on characteristic and feature/attribution of person. Fingerprint is, today, one of the biometric systems that have a wide range of use in many investigation areas. Fingerprint, especially used for authentication, is more reliable comparing to the other traditional methods which are used for access. In this study, a gray level co-occurrence matrix (GLCM) based fingerprint recognition system which provides successful results in tissue type imagining recognition has been implemented. The purpose of this study is to show the effectiveness of the GLCM in fingerprint recognition. By using GLCM which is a feature extracting method, fingerprint images are classified by multilayer perceptron (MLP) artificial neural network classification technique. Statistical methods were used to extraction the feature by obtaining the GLCM matrix for the gray level images. In the first step of system analysis, the system is trained by using GLCM attribute parameters and performance information is measured for different network topologies of the MLP classifier. After the classification stage, when the results are compared with the success rates of previously made fingerprint recognition systems, the success rate of 88.25% is considered as acceptable. As a result, it is considered that the results are reasonable when results are compared with other studies in the literature. Experimental results have also shown that the proposed method can improve the accuracy of existing methods.
