This work proposes an embedded data infrastructure based on ESP32 microcontrollers integrated with ESP32-CAM modules, focusing on the construction, management, and utilization of a centralized facial image database for emotion-based student readiness assessment. In the proposed architecture, multiple ESP32-CAM devices operate as distributed embedded sensing nodes, capturing facial images of students during academic assessment activities. Each embedded node performs lightweight preprocessing tasks, including image resizing and noise reduction, to optimize transmission efficiency and reduce network load. The preprocessed images are transmitted via Wi-Fi to a centralized database system, where they are securely stored and indexed for subsequent analysis. The database serves as a core component for large-scale data organization, enabling automated processing, historical tracking, and statistical aggregation of emotional data. A convolutional neural network (CNN), trained on the FER2013 dataset, analyzes the stored images to infer facial emotion categories, which are subsequently mapped to quantitative indicators of concentration and nervousness. This separation of embedded data acquisition from centralized analysis allows improved scalability, efficient resource utilization, and flexibility for future model updates without modifications to the embedded hardware. Experimental results demonstrate that the ESP32-CAM platform provides reliable long-term operation and consistent image quality in classroom-like environments. The proposed architecture highlights the role of embedded systems not only as data acquisition devices but as fundamental components of data-centric, emotion-aware educational platforms.