Achieving state-of-the-art results in face verification systems typically hinges on the availability of labeled face training data, a resource that often proves challenging to acquire in substantial quantities. In this research endeavor, we proposed employing Siamese networks for face recognition, eliminating the need for labeled face images. We achieve this by strategically leveraging negative samples alongside nearest neighbor counterparts, thereby establishing positive and negative pairs through an unsupervised methodology. The architectural framework adopts a VGG encoder, trained as a double branch siamese network. Our primary aim is to circumvent the necessity for labeled face image data, thus proposing the generation of training pairs in an entirely unsupervised manner. Positive training data are selected within a dataset based on their highest cosine similarity scores with a designated anchor, while negative training data are culled in a parallel fashion, though drawn from an alternate dataset. During training, the proposed siamese network conducts binary classification via cross-entropy loss. Subsequently, during the testing phase, we directly extract face verification scores from the network's output layer. Experimental results reveal that the proposed unsupervised system delivers a performance on par with a similar but fully supervised baseline.

翻译：在人脸验证系统中达到最优性能通常依赖于标注人脸训练数据的可用性，而获取大量此类数据往往极具挑战。本研究提出采用孪生网络进行人脸识别，从而消除对标注人脸图像的依赖。我们通过策略性地利用负样本与最近邻对应样本，以无监督方式建立正负样本对来实现上述目标。架构框架采用VGG编码器，训练为双分支孪生网络。主要目标在于规避对标注人脸图像数据的需求，因此提出完全无监督的训练对生成方法：正训练数据依据与指定锚点的最高余弦相似度得分从数据集中选取，负训练数据则以类似方式从另一数据集中遴选。训练过程中，所提出的孪生网络通过交叉熵损失执行二分类任务。在测试阶段，我们直接从网络输出层提取人脸验证得分。实验结果表明，所提出的无监督系统的性能与类似的完全监督基线方法相当。