Gender Classification Using Smartphone Sensors and Machine Learning Approaches

Basit, Abdul W.; Khan, Muhammad Yaseen; Ali, Syed Sarmad; Suffian, Muhammad; Wajid, Abdul; Khan, Sumra

doi:10.1109/majicc56935.2022.9994132

Cited by 1 publication

(1 citation statement)

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“…They used data from HealthBoard.com and achieved an accuracy rate of 90.00% with the SVM classifier. Basit et al [12] introduced a gender classification method using smartphone sensors. Their method was based on the Monte Carlo method.…”

Section: Introductionmentioning

confidence: 99%

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

2023

Proceeding Book of 1st International Conference on Recent and Innovative Results in Engineering and Technology Icriret 2023

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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.

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Section: Introductionmentioning

confidence: 99%

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

2023

Proceeding Book of 1st International Conference on Recent and Innovative Results in Engineering and Technology Icriret 2023

View full text Add to dashboard Cite

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Gender Classification Using Smartphone Sensors and Machine Learning Approaches

Cited by 1 publication

References 30 publications

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

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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