CommuNety: deep learning-based face recognition system for the prediction of cohesive communities

Syed Afaq Ali Shah, Weifeng Deng, Muhammad Aamir Cheema, Abdul Bais

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities.

Original languageEnglish
Pages (from-to)10641-10659
Number of pages19
JournalMultimedia Tools and Applications
Volume82
Issue number7
Early online date10 Sept 2022
DOIs
Publication statusPublished - Mar 2023
Externally publishedYes

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