Abstract
Embedding methods have achieved success in face recognition by comparing facial features in a latent semantic space. However, in a fully unconstrained face setting, the features learned by the embedding model could be ambiguous or may not even be present in the input face, leading to noisy representations. We propose Probabilistic Face Embeddings (PFEs), which represent each face image as a Gaussian distribution in the latent space. The mean of the distribution estimates the most likely feature values while the variance shows the uncertainty in the feature values. Probabilistic solutions can then be naturally derived for matching and fusing PFEs using the uncertainty information. Empirical evaluation on different baseline models, training datasets and benchmarks show that the proposed method can improve the face recognition performance of deterministic embeddings by converting them into PFEs. The uncertainties estimated by PFEs also serve as good indicators of the potential matching accuracy, which are important for a risk-controlled recognition system.
Abstract (translated)
嵌入方法通过比较潜在语义空间中的人脸特征，取得了人脸识别的成功。但是，在完全不受约束的面设置中，嵌入模型学习的特征可能不明确，甚至可能不存在于输入面中，从而导致噪声表示。我们提出了概率人脸嵌入（pfes），它将每个人脸图像表示为潜在空间中的高斯分布。分布的平均值估计最可能的特征值，而方差显示特征值的不确定性。然后可以自然地导出概率解，以便使用不确定性信息匹配和融合PFE。对不同的基线模型、训练数据集和基准点进行的实证分析表明，该方法可以通过将确定性嵌入转化为pfes来提高人脸识别性能。PFES估计的不确定性也可以作为潜在匹配精度的良好指标，对风险控制识别系统具有重要意义。
URL
https://arxiv.org/abs/1904.09658
