<![CDATA[The fingerprint-based attendance system at the Makassar Public Health Laboratory (BLKM Makassar) has limitations because it only records administrative attendance and cannot verify the physical presence of employees in the workplace. This study aims to design and implement an integrated fingerprint attendance and Internet of Things (IoT)-based computer vision system to support automated employee presence monitoring. The system uses two ESP32-CAM units installed in the lobby area and connected to a Flask Python server and MariaDB database. Image processing employs Histogram of Oriented Gradients (HOG) for face localization, dlib 68 facial landmarks for face alignment, and ResNet-34 to generate 128-dimensional face embeddings. Face identification is performed using Euclidean Distance with a threshold of 0.45 and a Time Window Buffer mechanism during monitoring cycles at 10:00 and 14:00 WITA. The results show that image transmission through HTTP POST operates with HTTP 200 OK and a latency of approximately 80 ms. Under normal walking conditions, the system achieved a 100% detection success rate. From 150 dynamic tests, the multi-camera system achieved an overall detection success rate of 63.33%, while Camera 1 and Camera 2 achieved 60.67% and 40.00%, respectively. The system achieved a 74% success rate under fast walking conditions, which decreased to 16% during running due to motion blur and lighting conditions. The integration of fingerprint attendance and computer vision provides supporting information for identifying discrepancies between administrative attendance records and the visual presence of employees within the monitored camera-covered area.]]>
