LaED: a novel lightweight, edge-aware and explainable deep learning model for privacy-preserving facial attendance tracking in resource-constrained educational environments
https://www.nature.com/articles/s41598-026-42051-8
Publish Date: 2026-05-15 12:50:00
Source Domain: www.nature.com
This section provides an extensive evaluation of the LaED framework across recognition accuracy, spoof and deepfake resistance, robustness to adversarial perturbations, cross-domain generalization, computational efficiency, fairness, federated learning performance, and usability. The goal is to determine whether LaED meaningfully improves on existing lightweight and tracking-based approaches, and whether the gains are consistent across demographics, datasets, device types, and learning configurations. The evaluation integrates both tabular and graphical analysis and includes comparison with strong baselines used in real-world attendance systems.
Additional scalability and communication-efficiency analyses are presented in Appendix 2.
Overall recognition performance
LaED achieved the strongest overall performance across all recognition metrics, maintaining both high accuracy and low statistical variance under classroom-like conditions. As shown in Table 1, LaED reached an accuracy of 97.8% with a 95% confidence interval of 97.1 to 98.4 and a low standard deviation of 0.67. This indicates that performance remains consistent across bootstrap resamples and is not sensitive to particular subsets of the data. Figure 5 shows a visual presentation of the result. In comparison, prior systems such as Bugingo et al.1 and Surantha and Sugijakko2 show wider confidence intervals and higher variance, reflecting less stability when illumination changes, partial occlusions occur or students move rapidly. ArcFace remained competitive in raw accuracy but exhibited greater spread in its CI and SD values, suggesting less reliability when tested under the same classroom conditions.
Beyond accuracy, LaED also recorded the highest precision, recall and F1-scores, demonstrating that improvements are not limited to a single metric but extend to balanced classification performance. This is particularly important in classroom deployments where false positives can inflate attendance incorrectly…
