<commit_before># CVPR15-CFSS
------
Released on July 25, 2015

## Description

This is the implementation of Shizhan Zhu et al.'s CVPR-15 work [Face Alignment by Coarse-to-Fine Shape Searching](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhu_Face_Alignment_by_2015_CVPR_paper.pdf). It is open source under BSD-3 license (see the `LICENSE` file). Codes can be used freely only for acedemic purpose. If you want to apply it to industrial products, please send an email to <NAME> at `<EMAIL>` first.

## Demo Video
We have uploaded our [demo video](http://youtu.be/S4PQ63duO-I) in youtube. The trained model should perform with similar performance as shown in the demo video. Otherwise, the software might have been used in an inappropriate way.

## Citation
If you use the codes as part of your research project, please cite our work as follows:
```
@inproceedings{zhu2015face,
  title={Face Alignment by Coarse-to-Fine Shape Searching},
  author={<NAME> and <NAME> and <NAME> and <NAME>},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={4998--5006},
  year={2015}
}
```

## Dependency
Due to license issues, this implementation uses two publicly avaiable software packages as opposed to our original implementation: 
1. [VLFeat](https://github.com/vlfeat/vlfeat)
2. [LibSVM](https://github.com/cjlin1/libsvm).

## Installation
1. Put all the required dependency packages under the `external` folder. Matlab compatible bainary files must exist. Please note we have put `vl_sift_toosimple.c` in the `codes_released/feat/SIFT` folder. Please put it into the directory of `external/vlfeat-0.9.20/toolbox/sift` before compiling (and hence its relative Matlab compatible binary file would appear after compiling). This would slightly speed up sift extraction.
2. Put all the 3148 (for training) + 689 (for testing) in the `data` folder. All images could be downloaded at [IBUG page](http://ibug.doc.ic.ac.uk/resources/facial-point-annotations/). Only LFPW, Helen, AFW and IBUG set are required. Please change one of the filename in IBUG set from `image_092⋅_01.jpg` to `image_092_01.jpg` to avoid space in file name.

## Training and Testing
To train the model using the default setting, simply use the following command:
```matlab
>> getParametricModels; addAll; learnCFSS;
```
Model files would be expected to appear in the `model` folder.<commit_msg>new folders<commit_after># CVPR15-CFSS
------
Released on July 25, 2015

## Description

This is the implementation of <NAME> et al.'s CVPR-15 work [Face Alignment by Coarse-to-Fine Shape Searching](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhu_Face_Alignment_by_2015_CVPR_paper.pdf). It is open source under BSD-3 license (see the `LICENSE` file). Codes can be used freely only for acedemic purpose. If you want to apply it to industrial products, please send an email to <NAME> at `<EMAIL>` first.

## Demo Video
We have uploaded our [demo video](http://youtu.be/S4PQ63duO-I) in youtube. The trained model should perform with similar performance as shown in the demo video. Otherwise, the software might have been used in an inappropriate way.

## Citation
If you use the codes as part of your research project, please cite our work as follows:
```
@inproceedings{zhu2015face,
  title={Face Alignment by Coarse-to-Fine Shape Searching},
  author={<NAME> and <NAME> and <NAME>},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={4998--5006},
  year={2015}
}
```

## Dependency
Due to license issues, this implementation uses two publicly avaiable software packages as opposed to our original implementation: 
1. [VLFeat](https://github.com/vlfeat/vlfeat)
2. [LibSVM](https://github.com/cjlin1/libsvm).

## Installation
1. Put all the required dependency packages under the `external` folder. Matlab compatible bainary files must exist. Please note we have put `vl_sift_toosimple.c` in the `codes_released/feat/SIFT` folder. Please put it into the directory of `external/vlfeat-0.x.xx/toolbox/sift` before compiling (and hence its relative Matlab compatible binary file would appear after compiling). This would slightly speed up sift extraction.
2. Put all the 3148 (for training) + 689 (for testing) in the `data` folder. All images could be downloaded at [IBUG page](http://ibug.doc.ic.ac.uk/resources/facial-point-annotations/). Only LFPW, Helen, AFW and IBUG set are required. Please change one of the filename in IBUG set from `image_092⋅_01.jpg` to `image_092_01.jpg` to avoid space in file name.

## Training and Testing
To train the model using the default setting, simply use the following command:
```matlab
>> getParametricModels; addAll; learnCFSS;
```
Model files would be expected to appear in the `model` folder.
{'fraction_non_alphanumeric': 0.0912106135986733, 'fraction_numerical': 0.025912106135986733, 'mean_word_length': 4.764635603345281, 'pattern_counts': {'":': 0, '<': 15, '<?xml version=': 0, '>': 19, 'https://': 4, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6128431', 'n_tokens_mistral': 1519, 'n_tokens_neox': 1381, 'n_words': 605}