Automated Attendee Recognition System for Large-Scale Social Events or Conference Gathering

Manual attendance tracking at large-scale events, such as marriage functions or conferences, is often inefficient and prone to human error. To address this challenge, we propose an automated, cloud-based attendance tracking system that uses cameras mounted at the entrance and exit gates. The mounted cameras continuously capture video and send the video data to cloud services to perform real-time face detection and recognition. Unlike existing solutions, our system accurately identifies attendees even when they are not looking directly at the camera, allowing natural movements, such as looking around or talking while walking. To the best of our knowledge, this is the first system to achieve high recognition rates under such dynamic conditions. Our system demonstrates overall 90% accuracy, with each video frame processed in 5 seconds, ensuring real time operation without frame loss. In addition, notifications are sent promptly to security personnel within the same latency. This system achieves 100% accuracy for individuals without facial obstructions and successfully recognizes all attendees appearing within the camera's field of view, providing a robust solution for attendee recognition in large-scale social events.

Rice Grain Size Measurement using Image Processing

The rice grain quality can be determined from its size and chalkiness. The traditional approach to measure the rice grain size involves manual inspection, which is inefficient and leads to inconsistent results. To address this issue, an image processing based approach is proposed and developed in this research. The approach takes image of rice grains as input and outputs the number of rice grains and size of each rice grain. The different steps, such as extraction of region of interest, segmentation of rice grains, and sub-contours removal, involved in the proposed approach are discussed. The approach was tested on rice grain images captured from different height using mobile phone camera. The obtained results show that the proposed approach successfully detected 95\% of the rice grains and achieved 90\% accuracy for length and width measurement.