Face Recognition System Using a Hybrid Scale Invariant Feature Transform Based on Local Binary Pattern

2020 ◽  
pp. 794-804
Author(s):  
M. Koteswara Rao ◽  
K. Veera Swamy ◽  
K. Anitha Sheela
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Face Recognition System ◽  
Feature Transform ◽  
Scale Invariant Feature

Image Copy–Move Forgery Detection Using Combination of Scale-Invariant Feature Transform and Local Binary Pattern Features

2021 ◽  
Author(s):  
Marziye Shahrokhi ◽  
Alireza Akoushideh ◽  
Asadollah Shahbahrami
Keyword(s):  
Local Binary Pattern ◽  
Digital Images ◽  
Scale Invariant Feature Transform ◽  
Combination Method ◽  
Forgery Detection ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Copy Move Forgery Detection ◽  
Scale Invariant Feature

Today, manipulating, storing, and sending digital images are simple and easy because of the development of digital imaging devices from hardware and software points of view. Digital images are used in different contexts of people’s lives such as news, forensics, and so on. Therefore, the reliability of received images is a question that often occupies the viewer’s mind and the authenticity of digital images is increasingly important. Detecting a forged image as a genuine one as well as detecting a genuine image as a forged one can sometimes have irreparable consequences. For example, an image that is available from the scene of a crime can lead to a wrong decision if it is detected incorrectly. In this paper, we propose a combination method to improve the accuracy of copy–move forgery detection (CMFD) reducing the false positive rate (FPR) based on texture attributes. The proposed method uses a combination of the scale-invariant feature transform (SIFT) and local binary pattern (LBP). Consideration of texture features around the keypoints detected by the SIFT algorithm can be effective to reduce the incorrect matches and improve the accuracy of CMFD. In addition, to find more and better keypoints some pre-processing methods have been proposed. This study was evaluated on the COVERAGE, GRIP, and MICC-F220 databases. Experimental results show that the proposed method without clustering or segmentation and only with simple matching operations, has been able to earn the true positive rates of 98.75%, 95.45%, and 87% on the GRIP, MICC-F220, and COVERAGE datasets, respectively. Also, the proposed method, with FPRs from 17.75% to 3.75% on the GRIP dataset, has been able to achieve the best results.

scholarly journals Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding

Diagnostics ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 56 ◽  
Author(s):  
Soheila Gheisari ◽  
Daniel Catchpoole ◽  
Amanda Charlton ◽  
Zsombor Melegh ◽  
Elise Gradhand ◽  
...  
Keyword(s):  
Local Binary Pattern ◽  
Scale Invariant Feature Transform ◽  
Support Vector ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Encoding ◽  
Histological Images ◽  
Feature Transform ◽  
Scale Invariant Feature

Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more features some of which may not be recognizable by human eyes. This paper, proposes a combination of Scale Invariant Feature Transform with feature encoding algorithm to extract highly discriminative features. Then, distinctive image features are classified by Support Vector Machine classifier into five clinically relevant classes. The advantage of our model is extracting features which are more robust to scale variation compared to the Patched Completed Local Binary Pattern and Completed Local Binary Pattern methods. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our approach identified features that outperformed the state-of-the-art on both our neuroblastoma dataset and a benchmark breast cancer dataset. Our method shows promise for classification of neuroblastoma histological images.

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