Feb. 12, 2024, 5:10 a.m. | Enoch Solomon Abraham Woubie Eyael Solomon Emiru

cs.CR updates on arXiv.org arxiv.org

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 …

art availability cs.ai cs.cr cs.cv data deep learning endeavor face recognition face verification images network networks recognition research resource results state systems training training data verification verification systems

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