Nonlinear Hierarchical Part-Based Regression for Unconstrained Face Alignment
Authors: Xiang Yu, Zhe Lin, Shaoting Zhang, Dimitris N. Metaxas
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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 deļ¬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. |