Self-Reinforced Cascaded Regression for Face Alignment
Authors: Xin Fan, Risheng Liu, Kang Huyan, Yuyao Feng, Zhongxuan Luo
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments were performed on six widely used datasets include FRGC v2.0, LFPW, HELEN, AFW, i BUG and 300W. All faces are labeled 68 landmarks. We compute the alignment error for testing images using the standard mean error normalized by the inter-pupil distance (NME). |
| Researcher Affiliation | Academia | 1DUT-RU International School of Information Science & Engineering, Dalian University of Technology, Dalian, China 2Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China 3School of Mathematical Science, Dalian University of Technology, Dalian, China {xin.fan, rsliu, zxluo}@dlut.edu.cn, huyankang@hotmail.com yyaofeng@gmail.com |
| Pseudocode | No | The paper includes mathematical formulations and figures but no explicitly labeled "Pseudocode" or "Algorithm" blocks with structured steps. |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code or provide any links to a code repository. |
| Open Datasets | Yes | The experiments were performed on six widely used datasets include FRGC v2.0, LFPW, HELEN, AFW, i BUG and 300W. and The 300W set consisting of the test sets of LFPW and Helen (Le et al. 2012). |
| Dataset Splits | Yes | We started from 100 labeled examples, and implemented the self-reinforced version of LBF (SR-LBF) to automatically include 711 extra samples (regarded as unlabeled). and In contrast, our self-reinforced LBF (SR-LBF) starts from only a half of LFPW, i.e. 400 training labels, and the other half are included by our self-reinforced strategy. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions various algorithms and feature descriptors but does not list any specific software dependencies (e.g., libraries, frameworks) along with their version numbers. |
| Experiment Setup | No | The paper mentions some parameters like μ and λ but does not provide specific hyperparameter values (e.g., learning rate, batch size, epochs, optimizer settings) or detailed training configurations for the experimental setup. |