Smart Shopping Carts: Reducing Checkout Queues with RFID and AWS Facial Recognition

This study evaluates the efficiency of an experimental checkout system compared to traditional supermarket checkout processes through trials conducted during low and high activity periods. The experimental system demonstrated significant efficiency improvements, reducing checkout times by approximately 73% during low activity and 84% during high activity. Survey data revealed that younger users (18–30 years) rated the UI (User Interface) friendliness highest at 8.70 and showed a 100% preference for the new system, experiencing fewer facial recognition issues and finding product offers largely relevant. Conversely, older users (51–70 years) preferred traditional systems, faced more facial recognition challenges, and rated the UI friendliness lower. These findings suggest the new system is effective and well-received by younger users but requires enhancements to better accommodate older users, underscoring its potential to improve the overall shopping experience while highlighting areas for broader acceptance.

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Publication Details

Published
2024-11-06
DOI
https://doi.org/10.1109/intercon63140.2024.10833475
Citations
2
Primary Topic
IoT and GPS-based Vehicle Safety Systems
Type
article
Field-Weighted Citation Impact
0.43
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article

Smart Shopping Carts: Reducing Checkout Queues with RFID and AWS Facial Recognition

Victor M. Parasi Falcon, Rodrigo Toconas Ocaña, Sergio Andres Mego Peñaranda
2 citations
IoT and GPS-based Vehicle Safety Systems
0.43
article

Smart Shopping Carts: Reducing Checkout Queues with RFID and AWS Facial Recognition

Victor M. Parasi Falcon, Rodrigo Toconas Ocaña, Sergio Andres Mego Peñaranda
article en
2 citations

Abstract

This study evaluates the efficiency of an experimental checkout system compared to traditional supermarket checkout processes through trials conducted during low and high activity periods. The experimental system demonstrated significant efficiency improvements, reducing checkout times by approximately 73% during low activity and 84% during high activity. Survey data revealed that younger users (18–30 years) rated the UI (User Interface) friendliness highest at 8.70 and showed a 100% preference for the new system, experiencing fewer facial recognition issues and finding product offers largely relevant. Conversely, older users (51–70 years) preferred traditional systems, faced more facial recognition challenges, and rated the UI friendliness lower. These findings suggest the new system is effective and well-received by younger users but requires enhancements to better accommodate older users, underscoring its potential to improve the overall shopping experience while highlighting areas for broader acceptance.

Peruvian University of Applied Sciences (PE)
Openalex Percentile: Top 40%
IoT and GPS-based Vehicle Safety Systems
0.43
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