VisitTrack: Face Analytics for Retail Stores

Farha Anjum

Abstract: Retail businesses face ongoing challenges in understanding customer behavior to enhance in-store experiences and drive sales. Traditional methods of customer analytics, such as surveys or manual observation, are time- consuming and lack real-time insights. VisitTrack: Face Analytics for Retail Stores addresses this gap by leveraging advanced facial recognition and sentiment analysis technologies. This system captures real-time video streams from in-store cameras to analyze customer demographics, monitor foot traffic patterns, and assess emotional responses. By employing facial recognition, VisitTrack can accurately count unique visitors, identify age and gender distributions, and detect repeat customers without compromising privacy. Sentiment analysis algorithms monitor customers' emotional states, providing valuable insights into their satisfaction levels during their shopping journey. The data empowers retailers to optimize store layouts, personalize marketing strategies, and improve customer engagement. VisitTrack supports the generation of comprehensive reports, enabling data-driven decisions while maintaining robust anonymization techniques and compliance with GDPR. This paper demonstrates the potential of AI and computer vision to revolutionize retail analytics, empowering retailers to enhance customer experiences, increase operational efficiency, and drive sales growth in an increasingly digital marketplace.

Keywords: Facial recognition, sentiment analysis, retail analytics, customer behavior, computer vision

How to Cite?: Farha Anjum, "VisitTrack: Face Analytics for Retail Stores", Volume 14 Issue 10, October 2025, International Journal of Science and Research (IJSR), Pages: 1380-1389, https://www.ijsr.net/getabstract.php?paperid=SR251025092142, DOI: https://dx.doi.org/10.21275/SR251025092142