Overview of experimental models.

Face verification is important in a variety of applications, for instance, access control, surveillance, and identification. Existing methods often struggle with the challenges of dataset imbalance and manual hyperparameter tuning. To address this, we propose the Adaptive Margin Loss and Dual Path Network+ (AMD-FV) for deep face verification. Two innovations are introduced, namely, Adaptive Margin Loss (AML) and Dual Path Network+ (DPN+). AML aims at automating the selection of margin and scale hyperparameters in large margin loss functions, thus, eliminating the need for manual tuning. Input dissimilarity information is used to estimate the margin, while the scale parameter is computed using the number of classes and AML’s range. Next, DPN+ enhances the original Dual Path Network by redesigning the first block with a series of 3x3 convolutions, batch normalization, and ReLU activations, leveraging shared connections across layers, leading to increases in spatial resolution and computational cost efficiency, while maximizing the use of discriminative features. We present comprehensive experiments on five diverse face verification datasets (LFW, Megaface, IJB-B, CALFW, and CPLFW) to demonstrate the effectiveness of the proposed approach. The results show that AMD-FV outperforms state-of-the-art methods, achieving a verification accuracy of 99.75% on LFW, improving the True Acceptance Rate by 6% on IJB-B at a False Acceptance Rate of 0.001, compared to VGGFace2, and attaining a Rank-1 identification score of 92.16% on Megaface, surpassing the CosFace model by 9.44%.

人脸验证在诸多关键应用场景中具备重要价值，例如门禁控制、监控安防与身份核验。现有方法往往面临数据集不平衡与手动调优超参数的双重挑战。为此，我们提出适用于深度人脸验证任务的自适应边缘损失与双路径网络+方法（AMD-FV）。该方法包含两项核心创新：自适应边缘损失（Adaptive Margin Loss，AML）与改进型双路径网络+（Dual Path Network+，DPN+）。自适应边缘损失旨在实现大边缘损失函数中边缘系数与尺度超参数的自动化选择，从而无需手动调优；模型借助输入样本的相异性信息估算边缘系数，而尺度超参数则通过类别数量与自适应边缘损失的取值范围计算得到。改进型双路径网络+对原始双路径网络进行优化：通过重构初始模块，采用一系列3×3卷积、批量归一化（Batch Normalization）与ReLU激活函数，并利用层间共享连接机制，在提升空间分辨率与计算效率的同时，最大化区分性特征的提取效能。我们在五组多样化的人脸验证数据集（LFW、Megaface、IJB-B、CALFW与CPLFW）上开展了全面实验，以验证所提方法的有效性。实验结果表明，AMD-FV的性能优于当前顶尖方法：在LFW数据集上的验证准确率达99.75%；在误接受率（False Acceptance Rate，FAR）为0.001的条件下，相较于VGGFace2，IJB-B数据集上的真接受率（True Acceptance Rate，TAR）提升了6%；在Megaface数据集上的Rank-1识别精度达92.16%，较CosFace模型提升了9.44个百分点。