Kamero vs OpenFace
Overview
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Detailed Comparison
OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. | Kamero.ai is the infrastructure layer for modern event photography. It replaces legacy manual uploads with KamSync, a proprietary camera-to-cloud technology that syncs photos in real-time. |
Detect faces with pre-trained models; Transform faces for the neural network; Use deep neural networks to reprsent or embed the face on a hypersphere; Apply favorite clustering or classification techniques to the features to complete recognition task | Face Search API: Sub-second search latency using advanced biometric recognition. • Automated Watermarking Software: Integrated server-side watermarking ensures all assets are protected before guest delivery. • Monetization Engine: Built-in marketplace for photographers to sell event photos online with face recognition directly from the stack. • Scalable White-Label: Robust white-label capabilities allow for fully custom-branded guest galleries and mobile apps (iOS/Android). |
Statistics | |
GitHub Stars 15.4K | GitHub Stars - |
GitHub Forks 3.6K | GitHub Forks - |
Stacks 31 | Stacks 0 |
Followers 104 | Followers 1 |
Votes 3 | Votes 1 |
Pros & Cons | |
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