Data-specific Adaptive Threshold for Face Recognition and Authentication
This work addresses a specific bottleneck in face recognition for real-world applications, offering an incremental improvement in accuracy.
The paper tackles the challenge of determining optimal thresholds for face recognition systems in practical use, particularly for online registration, and introduces an adaptive threshold mechanism that achieves a 22% accuracy improvement on the LFW dataset.
Many face recognition systems boost the performance using deep learning models, but only a few researches go into the mechanisms for dealing with online registration. Although we can obtain discriminative facial features through the state-of-the-art deep model training, how to decide the best threshold for practical use remains a challenge. We develop a technique of adaptive threshold mechanism to improve the recognition accuracy. We also design a face recognition system along with the registering procedure to handle online registration. Furthermore, we introduce a new evaluation protocol to better evaluate the performance of an algorithm for real-world scenarios. Under our proposed protocol, our method can achieve a 22\% accuracy improvement on the LFW dataset.