We lifted 8 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ndido98/frcsyn | canonical | 7 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| distance | Ran | ndido98/frcsyn/utils.py pointer only (licence: NONE) · get_code("5ee46308f94853c9") |
| get_block | Ran | ndido98/frcsyn/backbone.py pointer only (licence: NONE) · get_code("e5b39eaf623e4d9c") |
| get_blocks | Ran | ndido98/frcsyn/backbone.py pointer only (licence: NONE) · get_code("f17de7333650d2d1") |
| multi_glob | Ran | ndido98/frcsyn/align_faces.py pointer only (licence: NONE) · get_code("5401803657401b0f") |
| multi_rglob | Ran | ndido98/frcsyn/align_faces.py pointer only (licence: NONE) · get_code("5f123c05be37539a") |
| normalize | Ran | ndido98/frcsyn/utils.py pointer only (licence: NONE) · get_code("ee9d0f57ff58e4d0") |
| select_best_bbox | Ran | ndido98/frcsyn/align_faces.py pointer only (licence: NONE) · get_code("6a4400694d974add") |
| build_model | Not yet run | ndido98/frcsyn/backbone.py pointer only (licence: NONE) · get_code("752f901b03b29535") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail. This paper offers an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first international challenge aiming to explore the use of synthetic data in face recognition to address existing limitations in the technology. Specifically, the FRCSyn Challenge targets concerns related to data privacy issues, demographic biases, generalization to unseen scenarios, and performance limitations in challenging scenarios, including significant age disparities between enrollment and testing, pose variations, and occlusions. The results achieved in the FRCSyn Challenge, together with the proposed benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2311.10476")
get_code_for_paper("2311.10476")
have("2311.10476")
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