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Yayınlar
- BildiriYalnız künye2015Improvement of Palmprint Coding Techniques with Feature Descriptors
Bingöl, Ozkan; Ekinci, Murat
In recent years, biometric palmprint recognition system is one of the preferred research areas due to reach high accuracy even with the low resolution cameras. In recent studies conducted in this area, especially due to hygiene problems, images are taken in contactless and unrestricted environment. Despite a very successful improving methods have been developed for the images obtained in the restricted environment, shifts or projective distortions that occur in the palm pattern obtained from images taken in unconstrained environments negatively affect the success of this method. In this study, coding techniques that are commonly used palmprint recognition systems (Competitive Code, Ordinal Code, Derivative of Gaussian Filters Code) are strengthened with feature determinants (SIFT, SURF) and a new approach has been proposed. Scope of the study, a new data set consists of low-resolution images taken in unconstrained environments created and palmprint patterns obtained with the help of Active Appearance Model. First, performance rate of these coding techniques on the patterns were analyzed. Then, the advantages of 6 different approaches obtained by combining each feature determinant with each coding technique, is disclosed to the system.
- BildiriYalnız künye2015Detection of Copy-Move Forgery Using DoGCode
Türk, Salih; Bingöl, Ozkan; Ulutas, Guzin
Copy-move forgery is performed as a part of an image is copied and moved to another region within the same image. For the detection of this type of forgery, usually, feature vectors of the image are created by dividing the image into sub-blocks. Then the feature vector of the region is compared with other regions in the image to reach a conclusion if they are matched. In this study, to determine the copy-move forgery, DoG Coding technique which is more resistant to changes in image brightness is used. Coded new image is formed with the help of this technique often preferred in the palmprint recognition systems and features are determined by dividing the image into sub-blocks. Block based region growing segmentation method have been developed to determine the size of the copied region. Proposed this new approach has been successfully applied on images in the CoMoFod dataset prepared by different scenarios.