  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.
