A New Technique in saving Fingerprint with low volume by using Chaos Game and Fractal Theory

Fingerprint is one of the simplest and most reliable biometric features of human for identification. In this study by using fractal theory and by the assistance of Chaos Game a new fractal is made from fingerprint. While making the new fractal by using Chaos Game mechanism some parameters, which can be used in identification process, can be deciphered. For this purpose, a fractal is made for each fingerprint, we save 10 parameters for every fingerprint, which have necessary information for identity, as said before. So we save 10 decimal parameters with 0.02 accuracy instead of saving the picture of a fingerprint or some parts of it. Now we improve the great volume of fingerprint pictures by using this model which employs fractal for knowing the personality

The Evaluation of Adherence to Dietary and Liquid Intake Recommendations in Hemodialysis Patients

Background: Non-adherence of hemodialysis patients to their recommended diet, and limitations on fluid intake, leads to the production of toxic substances and metabolites in the bloodstream, which can increase the risk of mortality in these patients. This study evaluated the adherence to dietary and liquid intake recommendations in hemodialysis patients. Methods: One hundred and four hemodialysis patients were evaluated for dietary intake and adherence to dietary recommendations. Their diatary practice was evaluated using a 95-item food frequency questionnaire, and the Dialysis Diet and Fluid Nonadherence Questionnaire (DDFQ) questionnaire was used to assess non-adherence to dietary and fluind intake reommendations. The amount of weight gain between two sessions of dialysis and biochemical parameters of the patients was also measured. Statistical analysis was performend using SPSS version 22. Results: Approximately 21.8% and 34.7% of patients did notadhere to their diet and of fluid intake recommendations. The rate of nonadherence to fluid intake recommendations were higher than the dietary recommendations. Protein intake of the patients was at an expected level, but their potassium intake was higher than the requirement of these patients. Conclusion: The number of days of non- adherence to the diet was less than that of the fluid intake recommendations. Also, the degree of nonadherence were more severe for diatary recommendations, compared to fluin intake limitations

Synthetic MSI Images of Georgian Palimpsests (SGP Dataset)

This is a dataset of Synthetic MSI Images of Georgian Palimpsests (SGP Dataset). It has been created for the purpose of training inpainting models in order to remove the overtext and reconstruct the undertext. It consists of three subsets: train, test and validation. Each synthetic palimpsest has its on mask and ground truth imagesas follows: Ground Truth Image: ImageName_a Mask Image: ImageName_b Synthetic Palimpsests: ImageName_c The typeface used to generate this synthetic dataset is for a very particular and unknown script to generate synthetic training samples. The first draft of the typeface was created by Jost Gippert in 2005, while the final version was prepared by Andreas Stötzner in 2007. The research for this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2176 ‘Understanding Written Artefacts: Material, Interaction and Transmission in Manuscript Cultures', project no. 390893796. The research was conducted within the scope of the Centre for the Study of Manuscript Cultures (CSMC) at Universität Hamburg

Pairwise linear regression: An efficient and fast multi-view facial expression recognition

Abstract — Multi-view facial expression recognition (MFER) is an active research topic in facial analysis. In fact, not only the accuracy but also time complexity is desirable for real applications. In this paper, we introduce a new fast and robust approach for recognizing facial expressions in arbitrary views. Our approach relies on learning linear regressions between pairs of non-frontal and frontal sets to virtually compensate occluded facial parts. We learn linear regression for projecting from non-frontal to frontal views. Such approx-imated frontal training features are applied for training view specific facial expression classifiers. We propose a number of different variants of our approach, including sparse encoding and ridge-regression for feature representation. While classical pose specific methods strongly depend on the quality of the pose estimation step, our approaches maintain their superior behavior even under severe pose noise. We evaluate on both BU3DFE and Multi-PIE datasets and outperform the state-of-the-art in classification accuracy, even with a simple pose specific baseline method, while being extremely robust to feature noise and erroneous viewpoint estimation with our pairwise regression approaches. I

A new technique in saving fingerprint with low volume by using Chaos Game and Fractal Theory

Fingerprint is one of the simplest and most reliable biometric features of human for identification. In this study by using fractal theory and by the assistance of Chaos Game a new fractal is made from fingerprint. While making the new fractal by using Chaos Game mechanism some parameters, which can be used in identification process, can be deciphered. For this purpose, a fractal is made for each fingerprint, we save 10 parameters for every fingerprint, which have necessary information for identity, as said before. So we save 10 decimal parameters with 0.02 accuracy instead of saving the picture of a fingerprint or some parts of it. Now we improve the great volume of fingerprint pictures by using this model which employs fractal for knowing the personality