In deep metric learning (DML), high-level input data are represented in a lower-level representation (embedding) space, such that samples from the same class are mapped close together, while samples from disparate classes are mapped further apart. In this lower-level representation, only a single inference sample from each known class is required to discriminate between classes accurately. The features a DML model uses to discriminate between classes and the importance of each feature in the training process are unknown. To investigate this, this study takes embeddings trained to discriminate faces (identities) and uses unsupervised clustering to identify the features involved in facial identity discrimination by examining their representation within the embedded space. This study is split into two cases; intra class sub-discrimination, where attributes that differ between a single identity are considered; such as beards and emotions; and extra class sub-discrimination, where attributes which differ between different identities/people, are considered; such as gender, skin tone and age. In the intra class scenario, the inference process distinguishes common attributes between single identities, achieving 90.0\% and 76.0\% accuracy for beards and glasses, respectively. The system can also perform extra class sub-discrimination with a high accuracy rate, notably 99.3\%, 99.3\% and 94.1\% for gender, skin tone, and age, respectively.

翻译：在深度度量学习（DML）中，高层输入数据被表示为低层表示（嵌入）空间，使得同一类别的样本被映射到相近位置，而不同类别的样本则被映射到更远位置。在这种低层表示中，只需每个已知类别的一个推理样本即可准确区分类别。DML模型用于区分类别的特征以及各特征在训练过程中的重要性尚不明确。为探究此问题，本研究采用经过人脸（身份）辨别训练的嵌入，通过无监督聚类方法分析嵌入空间中的表示，以识别参与人脸身份辨别的特征。研究分为两个场景：类内子区分——考察同一身份内部的不同属性（如胡须和情绪）；类间子区分——考察不同身份/人群之间的差异属性（如性别、肤色和年龄）。在类内场景中，推理过程可区分同一身份的共同属性，对胡须和眼镜的识别准确率分别达到90.0%和76.0%。该系统还能以高准确率执行类间子区分，其中性别、肤色和年龄的准确率分别达到99.3%、99.3%和94.1%。