Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Dual-Cross Central Difference Network for Face Anti-Spoofing
Authors: Zitong Yu, Yunxiao Qin, Hengshuang Zhao, Xiaobai Li, Guoying Zhao
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments are performed on four benchmark datasets with three testing protocols to demonstrate our state-of-the-art performance. |
| Researcher Affiliation | Academia | Zitong Yu1 , Yunxiao Qin2 , Hengshuang Zhao3 , Xiaobai Li1 and Guoying Zhao1 1CMVS, University of Oulu 2Northwestern Polytechnical University 3University of Oxford |
| Pseudocode | Yes | Algorithm 1 Patch Exchange Augmentation Input: Face images I with batchsize N, pseudo depth map labels D, augmented ratio γ [0, 1], step number ρ 1 : for each Ii and Di, i = 1, ..., γ N do 2 : for each step ρ do 3 : Randomly select a patch region P within Ii 4 : Randomly select a batch index j, j N 5 : Exchange the image patch Ii(P) = Ij(P) and label patch Di(P) = Dj(P) 6 : end 7: end 8: return augmented I and D |
| Open Source Code | No | The paper states 'Our proposed method is implemented with Pytorch.' but does not provide any explicit statement about releasing source code or a link to a repository. |
| Open Datasets | Yes | Four databases OULU-NPU [Boulkenafet et al., 2017], CASIA-MFSD [Zhang et al., 2012], Replay Attack [Chingovska et al., 2012] and Si W-M [Liu et al., 2019] are used in our experiments. |
| Dataset Splits | Yes | In OULU-NPU dataset, we follow the original protocols and metrics, i.e., Attack Presentation Classification Error Rate (APCER), Bona Fide Presentation Classification Error Rate (BPCER), and ACER for a fair comparison. |
| Hardware Specification | Yes | In the training stage, models are trained with batch size 8 and Adam optimizer on a single V100 GPU. |
| Software Dependencies | No | The paper states 'Our proposed method is implemented with Pytorch.' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | In the training stage, models are trained with batch size 8 and Adam optimizer on a single V100 GPU. Data augmentations including horizontal flip, color jitter and Cutout are used as baseline. The initial learning rate (lr) and weight decay are 1e-4 and 5e-5, respectively. We train models with maximum 800 epochs while lr halves in the 500th epoch. |