harveyslash/Facial-Similarity-with-Siamese-Networks-in-Pytorch
Implementing Siamese networks with a contrastive loss for similarity learning
This helps security professionals, researchers, or anyone working with identity verification to determine if two different images contain the same person's face. You provide folders of facial images, and the system learns to identify identical individuals. The output is a highly accurate model that can confirm or deny if two faces match.
990 stars. No commits in the last 6 months.
Use this if you need to build a system that can accurately recognize if two different photos show the same person, even with limited examples.
Not ideal if you need to identify a specific person from a large database of many individuals, rather than just comparing two faces.
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MIT
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Jul 16, 2020
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