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..
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. |