  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.
