Although deep learning are commonly employed for image recognition, usually
huge amount of labeled training data is required, which may not always be
readily available. This leads to a noticeable performance disparity when
compared to state-of-the-art unsupervised face verification techniques. In this
work, we propose a method to narrow this gap by leveraging an autoencoder to
convert the face image vector into a novel representation. Notably, the
autoencoder is trained to reconstruct neighboring face image vectors rather
than the original input image vectors. These neighbor face image vectors are
chosen through an unsupervised process based on the highest cosine scores with
the training face image vectors. The proposed method achieves a relative
improvement of 56\% in terms of EER over the baseline system on Labeled Faces
in the Wild (LFW) dataset. This has successfully narrowed down the performance
gap between cosine and PLDA scoring systems.

通过利用自动编码器将面部图像向量转化为新的表示，本研究提出一种缩小有标签训练数据差距的方法，从而在无监督的人脸验证技术方面取得了 56％的 EER 相对改进，成功缩小了余弦和 PLDA 评分系统之间的性能差距。