Aging presents a significant challenge in face recognition, as changes in skin texture and tone can alter facial features over time, making it particularly difficult to compare images of the same individual taken years apart, such as in long-term identification scenarios. Transformer networks have the strength to preserve sequential spatial relationships caused by aging effect. This paper presents a technique for loss evaluation that uses a transformer network as an additive loss in the face recognition domain. The standard metric loss function typically takes the final embedding of the main CNN backbone as its input. Here, we employ a transformer-metric loss, a combined approach that integrates both transformer-loss and metric-loss. This research intends to analyze the transformer behavior on the convolution output when the CNN outcome is arranged in a sequential vector. These sequential vectors have the potential to overcome the texture or regional structure referred to as wrinkles or sagging skin affected by aging. The transformer encoder takes input from the contextual vectors obtained from the final convolution layer of the network. The learned features can be more age-invariant, complementing the discriminative power of the standard metric loss embedding. With this technique, we use transformer loss with various base metric-loss functions to evaluate the effect of the combined loss functions. We observe that such a configuration allows the network to achieve SoTA results in LFW and age-variant datasets (CA-LFW and AgeDB). This research expands the role of transformers in the machine vision domain and opens new possibilities for exploring transformers as a loss function.

翻译：衰老给人脸识别带来了重大挑战，因为皮肤纹理和色调的变化会随时间改变面部特征，使得比较同一人物相隔数年拍摄的图像（例如在长期身份识别场景中）变得尤为困难。Transformer网络具备保留由衰老效应引起的序列空间关系的能力。本文提出一种损失评估技术，在人脸识别领域使用Transformer网络作为附加损失。标准度量损失函数通常以主CNN骨干网络的最终嵌入作为输入。在此，我们采用一种Transformer-度量损失，这是一种融合了Transformer损失与度量损失的组合方法。本研究旨在分析当CNN输出被排列为序列向量时，Transformer在卷积输出上的行为特性。这些序列向量具有克服因衰老产生的纹理或区域结构（即皱纹或皮肤松弛）的潜力。Transformer编码器的输入来自网络最终卷积层获得的上下文向量。学习到的特征可以更具年龄不变性，从而补充标准度量损失嵌入的判别能力。通过该技术，我们结合多种基础度量损失函数使用Transformer损失，以评估组合损失函数的效果。我们观察到，此类配置使网络在LFW及年龄变异数据集（CA-LFW和AgeDB）上取得了最先进的成果。本研究拓展了Transformer在机器视觉领域的作用，并为探索Transformer作为损失函数开辟了新的可能性。