Pose-Dependent Low-Rank Embedding for Head Pose Estimation
Authors: Handong Zhao, Zhengming Ding, Yun Fu
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on databases CMU-PIE, MIT-CBCL, and extended Yale B with different levels of random noise are conducted and six embedding model based baselines are compared. The consistent superior results demonstrate the effectiveness of our proposed method. |
| Researcher Affiliation | Academia | Handong Zhao , Zhengming Ding and Yun Fu Department of Electrical and Computer Engineering, Northeastern University, Boston, USA, 02115 College of Computer and Information Science, Northeastern University, Boston, USA, 02115 {hdzhao,allanding,yunfu}@ece.neu.edu |
| Pseudocode | Yes | Algorithm 1 Solving PLRE using ALM; Algorithm 2 PLRE for Head Pose Estimation |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Database: CMU-PIE (Sim, Baker, and Bsat 2003) [...] MIT-CBCL (Rowley, Baluja, and Kanade 1998; Alvira and Rifkin 2001) [...] Extended Yale B (Georghiades, Belhumeur, and Kriegman 2001) |
| Dataset Splits | Yes | For all experiments, five-fold cross-validation is applied as (Haj, Gonz alez, and Davis 2012). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU/GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | all the analytical experiments are conducted on CMU-PIE with the parameters set as m = 100, β = 10, γ = 1 and λ = 0.1. |