Learning Low-Rank Representations with Classwise Block-Diagonal Structure for Robust Face Recognition
Authors: Yong Li, Jing Liu, Zechao Li, Yangmuzi Zhang, Hanqing Lu, Songde Ma
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three public databases are performed to validate the effectiveness of our approach. The strong identification capability of representations with block-diagonal structure is verified. |
| Researcher Affiliation | Academia | 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2School of Computer Science, Nanjing University of Science and Technology 3University of Maryland, College Park |
| Pseudocode | Yes | Algorithm 1 Solving Problem (4) by Inexact ALM |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code will be made publicly available. |
| Open Datasets | Yes | We evaluate our approach on three public databases: the Extended Yale B database (Georghiades, Belhumeur, and Kriegman 2001) (Lee, Ho, and Kriegman 2005), the AR database (Martinez and Benavente. 1998) and the ORL database (Samaria and Harter 1994). |
| Dataset Splits | Yes | Following the protocol in DSLR (Zhang, Jiang, and Davis 2013), we first randomly select Nc images for each person as training images (Nc = 8, 32), and the rest as testing images. |
| Hardware Specification | Yes | All the experiments are performed on matlab with computer configuration as follows, CPU: i5-2400 3.10GHz RAM:14.0GB. |
| Software Dependencies | No | The paper states 'All the experiments are performed on matlab' but does not specify the version of Matlab or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Input: Feature Matrix X, parameter λ and α 1: Initialize: Z0 = 0, J0 = 0, E0 = 0, Y 0 1 = 0, Y 0 2 = 0, µ0 = 10 5, µmax = 108, ρ = 1.1, ε = 10 6 |