Understanding how deep neural networks make decisions is crucial for analyzing their behavior and diagnosing failure cases. In computer vision, a common approach to improve interpretability is to assign importance to individual pixels using post-hoc methods. Although they are widely used to explain black-box models, their fidelity to the model's actual reasoning is uncertain due to the lack of reliable evaluation metrics. This limitation motivates an alternative approach, which is to design models whose decision processes are inherently interpretable. To this end, we propose a face similarity metric that breaks down global similarity into contributions from restricted receptive fields. Our method defines the similarity between two face images as the sum of patch-level similarity scores, providing a locally additive explanation without relying on post-hoc analysis. We show that the proposed approach achieves competitive verification performance even with patches as small as 28x28 within 112x112 face images, and surpasses state-of-the-art methods when using 56x56 patches.

翻译：理解深度神经网络如何做出决策对于分析其行为及诊断失败案例至关重要。在计算机视觉领域，提高可解释性的常用方法是通过事后分析方法为单个像素分配重要性。尽管这些方法被广泛用于解释黑盒模型，但由于缺乏可靠的评估指标，它们对模型实际推理过程的忠实度尚不确定。这一局限性促使我们探索另一种途径，即设计决策过程本身具有可解释性的模型。为此，我们提出了一种人脸相似性度量方法，将全局相似性分解为受限感受野的贡献。我们的方法将两张人脸图像之间的相似性定义为局部块级相似性分数的总和，从而提供了一种不依赖事后分析的局部可加性解释。实验表明，即使在112×112人脸图像中使用小至28×28的图像块，所提方法仍能取得具有竞争力的验证性能；当使用56×56图像块时，其性能超越了现有最先进方法。