Open-set face recognition characterizes a scenario where unknown individuals, unseen during the training and enrollment stages, appear on operation time. This work concentrates on watchlists, an open-set task that is expected to operate at a low False Positive Identification Rate and generally includes only a few enrollment samples per identity. We introduce a compact adapter network that benefits from additional negative face images when combined with distinct cost functions, such as Objectosphere Loss (OS) and the proposed Maximal Entropy Loss (MEL). MEL modifies the traditional Cross-Entropy loss in favor of increasing the entropy for negative samples and attaches a penalty to known target classes in pursuance of gallery specialization. The proposed approach adopts pre-trained deep neural networks (DNNs) for face recognition as feature extractors. Then, the adapter network takes deep feature representations and acts as a substitute for the output layer of the pre-trained DNN in exchange for an agile domain adaptation. Promising results have been achieved following open-set protocols for three different datasets: LFW, IJB-C, and UCCS as well as state-of-the-art performance when supplementary negative data is properly selected to fine-tune the adapter network.

翻译：开集人脸识别描述了一种场景，其中训练和注册阶段未见过的未知个体在运行期间出现。本研究聚焦于监控名单这一开集任务，该任务需在低误识率下运行，且通常每个身份仅有少量注册样本。我们引入一种紧凑型适配器网络，它结合不同的代价函数（如对象球损失OS和提出的最大熵损失MEL）可从额外负样本中受益。MEL通过提升负样本的熵来改进传统交叉熵损失，并对已知目标类别附加惩罚以促进图库专业化。所提方法采用预训练的深度神经网络（DNN）作为人脸识别特征提取器。随后，适配器网络接收深度特征表示，替代预训练DNN的输出层以实现灵活的领域适配。在LFW、IJB-C和UCCS三个数据集上遵循开集协议取得了显著成果，当精心选择额外负样本对适配器网络进行微调时，性能达到当前最优水平。