Ai Closet:ai-powered Personal Wardrobe Management System

• Author(s):

  Prof S.B.Nimbekar | Siddharth Ovhal | Sandesh Rasal | Aditya Ghule | Pratik Deshmukh

• Keywords:

  Wardrobe Management, Artificial Intelligence, Outfit Recommendation, Google Vision API, React, Node.js, MongoDB, NextAuth, Color Harmony, Sustainability Tracking, Crowdfunding, UPI, Razorpay, HMAC-SHA256, Digital Payment Gateway.

• Abstract:

  This Paper Presents The Design, Development, And Evaluation Of AI Closet, An Intelligent Full-stack Web Application Built To Modernize Personal Wardrobe Management Through The Integration Of Artificial Intelligence, Cloud Computing, And Real-time Data Services. The System Addresses Six Common User Pain Points Including Wardrobe Disorganization, Inefficient Outfit Selection, Lack Of Sustainability Awareness, And Poor Shopping Decisions By Combining A React 18 Frontend With A Node.js And Express.js Backend, MongoDB For Persistent Storage, And Mongoose As The Object Document Mapper. Clothing Items Are Analyzed Automatically Using Google Cloud Vision API For Label Detection, Color Recognition, And Occasion Inference, While Images Are Stored And Delivered Through Cloudinary's Content Delivery Network. An Eight-mode Outfit Recommendation Engine Scores Wardrobe Items Using A Weighted Algorithm Incorporating Mood Profiling, Live Weather Conditions Fetched From A Real-time Weather API, Color Harmony Rules, And Learned User Preferences. Security Is Enforced Through Bcryptjs Password Hashing, JSON Web Token Based Stateless Authentication, And Role-based Access Control Distinguishing Regular Users From Administrators. Additional Features Include A Sustainability Impact Tracker Quantifying Water Saved And Carbon Emissions Prevented Through Clothing Donations, A Color Harmony Matcher Built On A Thirteen-color Rule Database, A Smart Wardrobe Gap Analyzer With E-commerce Integration, And A Calendar-based Outfit History System. Performance Evaluation Recorded An Average API Response Time Of 350 Milliseconds, A Page Load Time Of 2.4 Seconds, And A Mobile Accessibility Score Of 92 Out Of 100, Confirming That The Platform Is Production-ready And Scalable.

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Other Details

• Paper id:

  IJSARTV12I6105722

• Published in:

  Volume: 12 Issue: 6 June 2026

• Publication Date:

  2026-06-23

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