scholarly journals A Secure Boot Loader System for Loading an Operating System with Fingerprint Authentication

2019 ◽  
Vol 8 (10) ◽  
pp. 2368-2374
Keyword(s):  
Data Security ◽  
Directional Pattern ◽  
Random Forest Classifier ◽  
Accuracy Rate ◽  
Histograms Of Oriented Gradients ◽  
Local Directional Pattern ◽  
Technology Advancement ◽  
Different Types ◽  
Portable System ◽  
Storage Area

In today’s world, people are constantly on move and portable systems are in demand. With technology advancement, people exploit different types of memory devices for a portable system. For any external boot medium, the BIOS boot order setting change is required. The dynamic boot loader successfully eliminated this dependency and allowed the user to directly boot from any portable USB. The usage of USB has grown exponentially in recent years and securing it has become a major concern. In this paper, the USB is devised as a highly secured portable boot medium with fingerprint authentication to ensure data security. It performs feature extraction by combining both Local Directional Pattern (LDP) and Histograms of Oriented Gradients (HOG) which improves the accuracy rate. The classification is performed by random forest classifier, such that the intended users alone are granted access to the private storage area of the USB drive.

scholarly journals A Study on Pattern Recognition with the Histograms of Oriented Gradients in Distorted and Noisy Images

2020 ◽  
Vol 26 (4) ◽  
pp. 454-478
Author(s):  
Andrzej Bukała ◽  
Michał Koziarski ◽  
Bogusław Cyganek ◽  
Osman Koç ◽  
Alperen Kara
Keyword(s):  
Pattern Recognition ◽  
Negative Impact ◽  
Quality Data ◽  
High Quality ◽  
Denoising Method ◽  
High Quality Data ◽  
Histograms Of Oriented Gradients ◽  
Different Types ◽  
Simple Implementation ◽  
Salt And Pepper

Histograms of oriented gradients (HOG) are still one of the most frequently used low-level features for pattern recognition in images. Despite their great popularity and simple implementation performance of the HOG features almost always has been measured on relatively high quality data which are far from real conditions. To fill this gap we experimentally evaluate their performance in the more realistic conditions, based on images affected by different types of noise, such as Gaussian, quantization, and salt-and-pepper, as well on images distorted by occlusions. Different noise scenarios were tested such anti-distortions during training as well as application of a proper denoising method in the recognition stage. As underpinned with experimental results, the negative impact of distortions and noise on object recognition with HOG features can be significantly reduced by employment of a proper denoising strategy.

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