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AI-Powered Local Service Booking and Verification Web Application Using Face Authentication and Smart Recommendation System
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
DOI: https://doi.org/10.64388/IREV9I11-1717808
Abstract
The increasing demand for trusted local service providers — including plumbers, electricians, carpenters, and housekeeping staff — has exposed critical gaps in current booking platforms: fake service registrations, absence of identity verification, poor trust mechanisms, and delayed emergency service response. This paper presents the design and implementation of an AI-powered local service booking and verification web application that addresses these challenges through a multi-layered approach. The proposed system integrates face authentication using TensorFlow and Face API with government-issued ID verification to eliminate fraudulent registrations. A smart recommendation engine, built using collaborative and content-based filtering, suggests service providers to users based on ratings, location, and past booking behaviour. The platform supports real-time booking and scheduling, secure payment processing via Razorpay, a dynamic rating and review system, and emergency service request handling with priority routing. The backend is developed using Node.js and Express.js, the frontend with React.js, and MongoDB serves as the primary database. Google Apps Script is used for automated email notifications, and Firebase handles cloud storage for identity documents. Preliminary results demonstrate a face verification accuracy of 94.7%, a booking response time below 1.8 seconds, and a fraud prevention rate exceeding 91%. The platform provides a reliable, scalable, and low-cost solution for modernising local service ecosystems.
Keywords
Local Services, AI Verification, Face Authentication, Smart Recommendation, Service Booking, Emergency Services, Fraud Detection, Web Application, Google Apps Script, Machine Learning.
How to cite this paper
@article{1717808,
author = {M. Sivaneswara Perumal, M. Abdul Rahman, P. Tamilarasan, Dr. R. Dhamodharan},
title = {AI-Powered Local Service Booking and Verification Web Application Using Face Authentication and Smart Recommendation System},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {1806-1815},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717808.pdf},
abstract = {The increasing demand for trusted local service providers — including plumbers, electricians, carpenters, and housekeeping staff — has exposed critical gaps in current booking platforms: fake service registrations, absence of identity verification, poor trust mechanisms, and delayed emergency service response. This paper presents the design and implementation of an AI-powered local service booking and verification web application that addresses these challenges through a multi-layered approach. The proposed system integrates face authentication using TensorFlow and Face API with government-issued ID verification to eliminate fraudulent registrations. A smart recommendation engine, built using collaborative and content-based filtering, suggests service providers to users based on ratings, location, and past booking behaviour. The platform supports real-time booking and scheduling, secure payment processing via Razorpay, a dynamic rating and review system, and emergency service request handling with priority routing. The backend is developed using Node.js and Express.js, the frontend with React.js, and MongoDB serves as the primary database. Google Apps Script is used for automated email notifications, and Firebase handles cloud storage for identity documents. Preliminary results demonstrate a face verification accuracy of 94.7%, a booking response time below 1.8 seconds, and a fraud prevention rate exceeding 91%. The platform provides a reliable, scalable, and low-cost solution for modernising local service ecosystems.},
keywords = {Local Services, AI Verification, Face Authentication, Smart Recommendation, Service Booking, Emergency Services, Fraud Detection, Web Application, Google Apps Script, Machine Learning.},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717808}
}