AI-powered Body Type Analysis for Fashion Recommendation System

Sallar Sami Jogi; Bushra Khan; Safiullah jamali; Zafar Ali; Atta ur Rehman; Naseebullah; Shahzaib Ali

Authors

Sallar Sami Jogi Department of Computer Science, Quaid-e-Awam University of Engineering Science and Technology (QUEST), Nawabshah, Pakistan
Bushra Khan Department of Computer Science, Quaid-e-Awam University of Engineering Science and Technology (QUEST), Nawabshah, Pakistan
Safiullah jamali Department of Computer Science, Quaid-e-Awam University of Engineering Science and Technology (QUEST), Nawabshah, Pakistan
Zafar Ali Department of Computer Science, Quaid-e-Awam University of Engineering Science and Technology (QUEST), Nawabshah, Pakistan
Atta ur Rehman Department of Computer Science, Quaid-e-Awam University of Engineering Science and Technology (QUEST), Nawabshah, Pakistan
Naseebullah Department of Computer Science, Quaid-e-Awam University of Engineering Science and Technology (QUEST), Nawabshah, Pakistan
Shahzaib Ali Department of Computer Science, Quaid-e-Awam University of Engineering Science and Technology (QUEST), Nawabshah, Pakistan

Keywords:

Deep Learning, Body Type Analysis, Fashion Recommendation, CNN, Personalized Styling, E-commerce

Abstract

This paper presents an AI-powered fashion recommendation system that analyzes body types to offer personalized clothing suggestions. The system uses Convolutional Neural Networks (CNN) to classify male body shapes into three categories (ectomorph, mesomorph, endomorph) and matches them with suitable fashion items. We developed a web-based platform using the React-Django framework, allowing users to upload photos, receive a body type analysis, and get customized fashion advice. Testing shows our approach achieves a 94% success rate in body type classification, significantly outperforming existing methods. This study addresses a key gap in current fashion recommendation systems, which often overlook body type considerations for men. Our solution provides an effective and user-friendly way to enhance online shopping and build greater trust in fashion choices.

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