Dynamic Graph Representation for Occlusion Handling in Biometrics
Authors: Min Ren, Yunlong Wang, Zhenan Sun, Tieniu Tan11940-11947
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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). |