Cheng, T., Zhang, Y., Yin, Y., Zimmermann, R., Yu, Z., & Guo, B. (2023). A Multi-Teacher Assisted Knowledge Distillation Approach for Enhanced Face Image Authentication. Proceedings of the 2023 ACM International Conference on Multimedia Retrieval. https://doi.org/10.1145/3591106.3592280
Abstract:
Recent deep-learning-based face recognition systems have achieved significant success. However, most existing face recognition systems are vulnerable to spoofing attacks where a copy of the face image is used to deceive the authentication. A number of solutions are developed to overcome this problem by building a separate face anti-spoofing model, which however brings in additional storage and computation requirements. Since both recognition and face anti-spoofing tasks stem from the analysis of the same face image, this paper explores a unified approach to reduce the original dualmodel redundancy. To this end, we introduce a compressed multitask model to simultaneously perform both tasks in a lightweight manner, which has the potential to benefit lightweight IoT applications. Concretely, we regard the original two single-task deep models as teacher networks and propose a novel multi-teacher assisted knowledge distillation method to guide our lightweight multi-task model to achieve satisfying performance on both tasks. Additionally, to reduce the large gap between the deep teachers and the light student, a comprehensive feature alignment is further integrated by distilling multi-layer features. Extensive experiments are carried out on two benchmark datasets, where we achieve the task accuracy of 93% meanwhile reducing the model size by 97% and reducing the inference time by 56% compared to the original dual-model.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Ministry of Education (MOE) - Academic Research Fund Tier 2
Grant Reference no. : T2EP20221-0023
This work was supported in part by the National Natural Science Foundation of China (No. 61960206008, 62272390, 62025205,
62072375, 62032020).