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..
Nonlinear Hierarchical Part-Based Regression for Unconstrained Face Alignment
Authors: Xiang Yu, Zhe Lin, Shaoting Zhang, Dimitris N. Metaxas
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several challenging faces-in-the-wild datasets demonstrate the consistently better accuracy of our method, when compared to the state-of-the-art. We evaluate our method on six challenging benchmarks, i.e., Labeled Faces in the Wild (LFW) [Huang et al., 2007a], Labeled Facial Parts in the Wild (LFPW) [Belhumeur et al., 2011], Helen [Le et al., 2012], Annotated Faces-in-the-Wild (AFW) [Zhu and Ramanan, 2012], i Bug [Sagonas et al., 2013] and Caltech Occluded Faces in the Wild (COFW) [Burgos-Artizzu et al., 2013]. |
| Researcher Affiliation | Collaboration | NEC Laboratories America, Media Analytics Adobe Research ]University of North Carolina at Charlotte Rutgers, The State University of New Jersey |
| Pseudocode | Yes | Algorithm 1 The two-stage regression algorithm. |
| Open Source Code | No | The paper states: 'The codes are provided by the authors from internet.' and 'RCPR provides its training code in which the annotation can be deο¬ned by input data.' This refers to the code of *other* methods, not the authors' own code for the proposed method (HPR). |
| Open Datasets | Yes | We evaluate our method on six challenging benchmarks, i.e., Labeled Faces in the Wild (LFW) [Huang et al., 2007a], Labeled Facial Parts in the Wild (LFPW) [Belhumeur et al., 2011], Helen [Le et al., 2012], Annotated Faces-in-the-Wild (AFW) [Zhu and Ramanan, 2012], i Bug [Sagonas et al., 2013] and Caltech Occluded Faces in the Wild (COFW) [Burgos-Artizzu et al., 2013]. |
| Dataset Splits | No | The paper mentions training on LFPW and Helen but does not provide specific percentages or counts for training, validation, or test splits. It states: 'The training databases for HPR, RCPR and Co R are the same, which are LFPW and Helen.' |
| Hardware Specification | Yes | The average runtime on a 640 by 480 image is around 0.3s in Matlab with a dual core i7 3.4GHz CPU. |
| Software Dependencies | No | The paper mentions 'Matlab' but does not specify a version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | Typically the number of iterations is fixed to 4 or 5. We set the threshold angles to be 22.5 and 22.5 . When the training samples are not sufficient, we augment the initialization by rotation and random perturbation of global translation. Meanwhile, to prevent overfitting, denoting s = s s0, we modify Eq. 3 by adding the regularization terms, which is Eq. 10. |