<![CDATA[Body temperature is a critical health indicator, and non-contact thermal imaging-based prediction systems are essential for early detection of infectious diseases. This study compares the performance of three FA-SSD (Feature Fusion and Spatial Attention-Based Single Shot Detector) models with different feature extractors—VGG-19, MobileNetV3, and ResNet-152—for face detection and body temperature prediction using thermal images. A subset of 520 thermal facial images from the Comprehensive Facial Thermal Dataset was used, with 80% for training, 10% for validation, and 10% for testing. Model performance was evaluated using Generalized Intersection over Union (GIoU) for detection accuracy, and Mean Absolute Error (MAE) with Mean Absolute Percentage Error (MAPE) for temperature prediction accuracy. The results showed that no single model excelled in all metrics. ResNet-152 achieved the highest average GIoU, indicating superior object detection performance. VGG-19 delivered the lowest average MAE of 0.451°C and MAPE of 1.278%, making it the best for temperature prediction. MobileNetV3 achieved the lowest minimum MAE of 0.000050°C but showed higher average errors and validation fluctuations. In conclusion, VGG-19 is recommended for clinical temperature accuracy, ResNet-152 for robust detection, and MobileNetV3 for edge deployment.]]>
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