Comparative Analysis of Transfer Learning Architectures for Human Posture Classification Using Silhouette Images

Human posture classification is an important computer vision task for intelligent monitoring, rehabilitation, assistive systems, and human–machine interaction. This study presents a comparative evaluation of four transfer learning configurations, TL-ResNet-18, TL-ResNet-50, TL-MobileNet, and TL-Xception, under a common experimental protocol—for classifying four human postures (standing, sitting, lying, and bending) from silhouette images. The architectures were evaluated under a common experimental protocol using the same dataset partitioning, preprocessing, augmentation procedures, and principal training settings, while retaining architecture-specific fine-tuning configurations. Performance was assessed using accuracy, F1-score, and ROC-AUC across the training, validation, and test partitions. Among the evaluated architectures, TL-MobileNet achieved the strongest overall held-out classification performance, with a test accuracy of 93.47%, an F1-score of 93.00%, and a ROC-AUC of 99.25%. TL-Xception achieved the same test ROC-AUC of 99.25%, but a lower test accuracy of 92.08%, while TL-ResNet-18 and TL-ResNet-50 achieved test accuracies of 91.81% and 90.00%, respectively. The results demonstrate performance differences among the evaluated transfer learning configurations. Within the evaluated ResNet configurations, greater network depth did not correspond to improved classification performance under the evaluated conditions. Silhouette-based posture classification shows potential for supporting future human-centered smart applications while reducing reliance on appearance-rich imagery. Further evaluation is required to establish cross-dataset generalizability, computational efficiency, and real-time deployment performance.