AdaSin: Enhancing Hard Sample Metrics with Dual Adaptive Penalty for Face Recognition
Journal:
arXiv
Published Date:
Mar 5, 2025
Abstract
In recent years, the emergence of deep convolutional neural networks has
positioned face recognition as a prominent research focus in computer vision.
Traditional loss functions, such as margin-based, hard-sample mining-based, and
hybrid approaches, have achieved notable performance improvements, with some
leveraging curriculum learning to optimize training. However, these methods
often fall short in effectively quantifying the difficulty of hard samples. To
address this, we propose Adaptive Sine (AdaSin) loss function, which introduces
the sine of the angle between a sample's embedding feature and its ground-truth
class center as a novel difficulty metric. This metric enables precise and
effective penalization of hard samples. By incorporating curriculum learning,
the model dynamically adjusts classification boundaries across different
training stages. Unlike previous adaptive-margin loss functions, AdaSin
introduce a dual adaptive penalty, applied to both the positive and negative
cosine similarities of hard samples. This design imposes stronger constraints,
enhancing intra-class compactness and inter-class separability. The combination
of the dual adaptive penalty and curriculum learning is guided by a
well-designed difficulty metric. It enables the model to focus more effectively
on hard samples in later training stages, and lead to the extraction of highly
discriminative face features. Extensive experiments across eight benchmarks
demonstrate that AdaSin achieves superior accuracy compared to other
state-of-the-art methods.
