End-to-End Face Identification: A Comparative Study of Inception-ResNet-V1 and Swin Transformer Classifiers - Irvan Faiz Muhammad; Dwi Budi Santoso (ISB Atma Luhur, 2026)
Facial recognition technologies are increasingly being applied across various fields to facilitate human activities through automated systems. However, the existing frameworks often rely on multi-stage model pipelines, escalating computational complexity. This study compares two robust deep learning architectures, namely Inception-ResNet-V1 and Swin Transformer. Both are implemented as classifiers on the CASIA-WebFace dataset, consisting of 100 identity classes. The initial detector employs a cascaded network for multi-task learning (MTCNN). The Swin Transformer has a superior precision of 97.16%, surpassing the 96.35% attained by Inception-ResNet-V1. Furthermore, the high F1-scores of 96.7% and 95.79%, respectively, highlight an equilibrium alongside a robust approach to classifying a large number of classes. Beyond accuracy, both models exhibit lower latency in GPU environments, specifically 13.91 ms for the Swin Transformer and 15.04 ms for Inception-ResNet-V1. That marks a significant practical contribution to simplifying biometric identification by eliminating the necessity for separate feature extraction and distance matching modules. These results suggest that the end-to-end method holds immense possibilities for daily situations, including high-security authentication as well as large-scale automation surveillance, where computational robustness and efficiency are critical. Nevertheless, advanced optimization remains crucial for such a demanding environment.