Real-Time Human Identification and Crowd Management using AI and IoT
Kandula Sai Mahith, Medidi Sampath, Pinnam Lalitesh, Tata Jagannadh Swamy · 2025
Misuse of gender-specific public facilities remains a prevalent issue, often leading to discomfort, privacy breaches, security and time concerns. The current biometric authentication or RFID-based entry systems have been implemented, these solutions may introduce privacy concerns, require physical contact, or incur additional costs. For this aspect, we have proposed a novel system for Human Gender Identification and Crowd management that employs deep learning and computer vision for real-time gender detection. The proposed system integrates a pre-trained neural network model with an IoT-enabled ESP32 microcontroller, which triggers visual and audio alerts based on the identified gender. Also, the system facilitates smooth and continuous user flow by minimizing unnecessary queuing or clustering, ensuring orderly facility usage without manual intervention. This approach offers an automated, contactless, and cost-effective solution aimed at enhancing public safety and facility management with minimal human intervention.