We study the problem of performing face verification with an efficient neural model $f$. The efficiency of $f$ stems from simplifying the face verification problem from an embedding nearest neighbor search into a binary problem; each user has its own neural network $f$. To allow information sharing between different individuals in the training set, we do not train $f$ directly but instead generate the model weights using a hypernetwork $h$. This leads to the generation of a compact personalized model for face identification that can be deployed on edge devices. Key to the method's success is a novel way of generating hard negatives and carefully scheduling the training objectives. Our model leads to a substantially small $f$ requiring only 23k parameters and 5M floating point operations (FLOPS). We use six face verification datasets to demonstrate that our method is on par or better than state-of-the-art models, with a significantly reduced number of parameters and computational burden. Furthermore, we perform an extensive ablation study to demonstrate the importance of each element in our method.

翻译：我们研究了利用高效神经模型$f$进行人脸验证的问题。$f$的高效性源于将人脸验证问题从嵌入最近邻搜索简化为二分类问题：每个用户拥有其专属的神经网络$f$。为允许训练集中不同个体间的信息共享，我们不直接训练$f$，而是通过超网络$h$生成模型权重。这产生了可部署于边缘设备的紧凑型个性化人脸识别模型。该方法成功的关键在于一种生成困难负样本的新颖方式以及精心设计的训练目标调度策略。我们的模型实现了极小的$f$规模，仅需23k参数和5M浮点运算次数。我们在六个人脸验证数据集上证明，该方法在参数数量和计算负担大幅降低的情况下，性能与最先进模型相当甚至更优。此外，我们进行了全面的消融研究，以论证方法中每个要素的重要性。