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 [1].
Mixed Error Coding for Face Recognition with Mixed Occlusions
Authors: Ronghua Liang, Xiao-Xin Li
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the effectiveness and robustness of the proposed MEC model in dealing with mixed occlusions. |
| Researcher Affiliation | Academia | Ronghua Liang, Xiao-Xin Li Zhejiang University of Technology Hangzhou, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Mixed Error Coding (MEC) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We conduct a set of experiments on the Extended Yale B database [Georghiades et al., 2001] and the AR database [Martínez, 1998]. |
| Dataset Splits | Yes | For training, we use images from Subset I and II (717 images, with normal-to-moderate illumination conditions); for testing, we use images from Subset III (453 images, with extreme illumination conditions), Subset IV (524 images, with more extreme illumination conditions) and Subset V (712 images, with the most extreme illumination conditions), respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | The parameters of our MEC algorithm are selected as: λµ = 0, τ = 0.3,λs = 2 and σ = 0.75 in CIM. |