Privacy-Preserving Gender-Based Customer Behavior Analytics in Retail Spaces Using Computer Vision

In the competitive retail industry of the digital era, data-driven insights into gender-specific customer behavior are essential. They support the optimization of store performance, layout design, product placement, and targeted marketing. However, existing computer vision solutions often rely on facial recognition to gather such insights, raising significant privacy and ethical concerns. To address these issues, this paper presents a privacy-preserving customer analytics system through two key strategies. First, we deploy a deep learning framework using YOLOv9s, trained on the RCA-TVGender dataset. Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification. Second, we apply AES-128 encryption to customer position data, ensuring secure access and regulatory compliance. Our system achieved overall performance, with 81.5% mAP@50, 77.7% precision, and 75.7% recall. Moreover, a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers. For instance, women spent more time in certain areas and showed interest in different products. These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.