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..
Feature Generation and Hypothesis Verification for Reliable Face Anti-spoofing
Authors: Shice Liu, Shitao Lu, Hongyi Xu, Jing Yang, Shouhong Ding, Lizhuang Ma1782-1791
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show our framework achieves promising results and outperforms the state-of-the-art approaches on extensive public datasets. |
| Researcher Affiliation | Collaboration | Shice Liu1*, Shitao Lu1,2*, Hongyi Xu1,3, Jing Yang1 , Shouhong Ding1 , Lizhuang Ma2,3 1Youtu Lab, Tencent, Shanghai, China 2East China Normal University, Shanghai, China 3Shanghai Jiao Tong University, Shanghai, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source code is available at https://github.com/lustoo/FGHV. |
| Open Datasets | Yes | Above all, we conduct the cross-dataset testing on four public datasets, i.e., OULU-NPU (denoted as O) (Boulkenafet et al. 2017), CASIA-MFSD (denoted as C) (Zhang et al. 2012), Idiap Replay-Attack (denoted as I) (Chingovska et al. 2012) and MSU-MFSD (denoted as M) (Wen et al. 2015). After that, the cross-type testing is carried out on the rich-type dataset, i.e., Si W-M (Liu et al. 2019). |
| Dataset Splits | No | The paper states 'select one dataset for testing and the other three datasets for training' for cross-dataset testing and 'select out one attack type as the unknown testing type and treat the others as the known training types' for cross-type testing, but it does not explicitly provide details about a separate validation split or how it's derived. |
| Hardware Specification | Yes | All experiments are conducted via Py Torch on a 32GB Tesla-V100 GPU. |
| Software Dependencies | No | The paper mentions using 'Py Torch' but does not specify its version number or versions of other software dependencies. |
| Experiment Setup | Yes | During the training period, the framework is trained with SGD optimizer where the momentum is 0.9 and the weight decay is 5e-4. The learning rate is initially 1e-3 and drops to 1e-4 after 50 epochs. The hyper-parameters λ1 and λ2 are both set to 1. |