NP-PHOG: An Automated Gender Classification Model based on Nested Patch-based Prymadial Histogram-Oriented Gradients Feature Extraction using Shadow Images


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Authors

  • Turker Tuncer Firat University
  • Sengul Dogan Firat University

DOI:

https://doi.org/10.59287/icriret.1367

Keywords:

Shadow Gender Classification, Shadow Images, Nested Patches, PHOG, Digital Forensics

Abstract

Gender classification is a fundamental area of research in machine learning, and numerous types of data, such as gaits, faces, and speeches, have been utilized for gender classification. In this research, we introduce a novel data type, namely shadow images, for detecting gender. We collected a shadow image dataset comprising two classes, namely (1) female and (2) male. To propose an automated shadow gender classification model, we developed a pyramidal histogram-oriented gradient (PHOG) based model. Our model consists of three primary phases, including (i) feature extraction using nested patches and PHOG, (ii) neighborhood component analysis (NCA) based feature selection, and (iii) classification with support vector machine (SVM) classifier. Therefore, we have named our model nested patches (NP) based PHOG – NP-PHOG–. The proposed NP-PHOG model was applied to the collected shadow image dataset, and it achieved a classification accuracy of 99.69%. This result provides strong evidence that machine learning models can accurately detect gender using shadows, and new image forensic tools can be developed using this approach.

Author Biographies

Turker Tuncer, Firat University

Department of Digital Forensics Engineering, College of Technology, 23119, Elazig/ Turkey

Sengul Dogan, Firat University

Department of Digital Forensics Engineering, College of Technology, 23119, Elazig/ Turkey

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Published

2023-08-29

How to Cite

Tuncer, T., & Dogan, S. (2023). NP-PHOG: An Automated Gender Classification Model based on Nested Patch-based Prymadial Histogram-Oriented Gradients Feature Extraction using Shadow Images. International Conference on Recent and Innovative Results in Engineering and Technology, 29–35. https://doi.org/10.59287/icriret.1367