Predicting Gender from Human or Non-human Social Media Profile Photos by using Transfer Learning

被引：1

作者：

Sakib, Sadman ^{[1
]}

Fahad, Nur Mohammad ^{[1
]}

Raiaan, Mohaimenul Azam Khan ^{[1
]}

Rahman, Md. Anisur ^{[1
]}

Al Mamun, Abdullah ^{[1
]}

Islam, Salekul ^{[1
]}

Mukta, Md. Saddam Hossain ^{[1
]}

机构：

[1] UIU, Dept CSE, Plot 2,Madani Ave, Dhaka 1212, Bangladesh

来源：

2023 INTERNATIONAL CONFERENCE ON COMPUTER, ELECTRICAL & COMMUNICATION ENGINEERING, ICCECE | 2023年

关键词：

Social Media; Gender; Deep Learning; Convolutional Neural Network; Transfer Learning; CLASSIFICATION;

D O I：

10.1109/ICCECE51049.2023.10085525

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Social media profile photos can demonstrate a variety of information about a person, including her personality, behavior, preference, individuality, and gender. Prediction of gender from social media photos has a number of real life applications such as gender marketing and identification of camouflaged profile photos. Numerous techniques can be applied for determining gender from a user's profile photos. In this study, we predict a user's gender from her social media profile photos (i.e., Facebook, Twitter, and Instagram) by using multiple transfer learning models. While conventional methods are straightforward and can only determine gender based on human faces, we propose a novel model that determines gender based on both human faces and non-human pictures (i.e., a flower, animal, cartoon, doll, etc.). The model predicts the gender of a user based on the pattern of sharing profile photos with an outstanding accuracy of 95.75%.

引用

页数：7

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