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..
Dynamic Graph Representation for Occlusion Handling in Biometrics
Authors: Min Ren, Yunlong Wang, Zhenan Sun, Tieniu Tan11940-11947
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on iris and face demonstrate the superiority of the proposed framework, which boosts the accuracy of occluded biometrics recognition by a large margin comparing with baseline methods. |
| Researcher Affiliation | Academia | 1University of Chinese Academy of Sciences, 2CRIPAC NLPR CASIA, Beijing, P.R. China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Four databases are used for experiments: (1) ND Cross Sensor Iris 2013 Dataset-LG4000. It contains 29,986 iris samples from 1,352 classes. (2) CASIA Iris Image Database V4-Distance. It contains 2,446 iris samples from 284 classes. (3) CASIA-Iris-M1-S2. It contains 6,000 iris samples from 400 classes. (4) CASIA Iris Image Database V4-Lamp. This database contains 16,212 iris samples from 819 classes. [...] The CASIA-Web Face (Yi et al. 2014) is adopted as the training database. |
| Dataset Splits | No | The paper mentions training and testing sets, but does not explicitly detail a separate validation dataset split with specific percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper describes the architecture of the convolutional blocks (e.g., 'Conv Block 1 contains four convolutional layers'), but does not provide specific experimental setup details such as hyperparameters (learning rate, batch size, epochs, optimizers). |