CemiFace: Center-based Semi-hard Synthetic Face Generation for Face Recognition
Authors: Zhonglin Sun, Siyang Song, Ioannis Patras, Georgios Tzimiropoulos
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that with a modest degree of similarity, training on the generated dataset can produce competitive performance compared to previous generation methods. |
| Researcher Affiliation | Academia | Zhonglin Sun Queen Mary University of London zhonglin.sun@qmul.ac.uk Siyang Song University of Exeter ss2796@cam.ac.uk Ioannis Patras Queen Mary University of London i.patras@qmul.ac.uk Georgios Tzimiropoulos Queen Mary University of London g.tzimiropoulos@qmul.ac.uk |
| Pseudocode | Yes | The pseudo-code for training and generation are given in Supplementary Material Section A.3. ... Algorithm 1 The training pipeline of our Cemi Face ... Algorithm 2 The pipeline of Cemi Face-based face dataset generation |
| Open Source Code | Yes | The code will be available at:https://github.com/szlbiubiubiu/Cemi Face |
| Open Datasets | Yes | We first split face images in the CASIA-Web Face [36] into various levels of groups... For example, with the same data volume, the model trained on the state-of-the-art synthetic dataset DCface [24] produces 11.23% lower verification performance on CFP-FP testset than the model with the same architecture trained on the real dataset. ... We employ 3 datasets for training:(a) CASIAWeb Face as used in DCFace; (b) A challenging in-the-wild dataset Flickr with 1.2M images collected by us from Flickr website; (c) VGGFace2 [13] which is a large-scale dataset containing 3.3M clean images. |
| Dataset Splits | No | The paper references evaluation datasets (LFW, CFP-FP, Age DB-30, CPLFW, CALFW) for testing performance, but it does not explicitly define a separate 'validation' split or dataset used during the training process with specific percentages or sample counts. |
| Hardware Specification | Yes | The batch size is 160 on 2 A100 GPUs. |
| Software Dependencies | No | The paper mentions several models, methods, and optimizers (e.g., 'Cos Face [1]', 'Adam W [44]', 'DDPM [21, 22]', 'DDIM [22]', 'Ada GN [42]', 'UNet [34]'), but it does not specify concrete version numbers for any software libraries or frameworks (e.g., Python, PyTorch, TensorFlow versions) required for replication. |
| Experiment Setup | Yes | Specifically, the margin of Cosface is 0.4, weight decay is 5e-4, learning rate is 1e-1 and is decayed by 10 at the 26th and 34th epoch, totally the model is trained for 40 epochs. We add random resize & crop with the scale of [0.9, 1.0], Random Erasing with the scale of [0.02,0.1], and random flip. Brightness, contrast, saturation and hue are all set to be 0.1. ... The maximum time step T for diffusion training is 1000. Then for generating the synthetic face recognition dataset, the time step for DDIM [22] is 20. ... Specifically, in the mini-batch, we assign a randomly selected m from -1 to 1 with an interval of 0.02 |