# Joint-Face-Detection-and-Alignment

**Repository Path**: abc0317/Joint-Face-Detection-and-Alignment

## Basic Information

- **Project Name**: Joint-Face-Detection-and-Alignment
- **Description**: https://github.com/luoyetx/Joint-Face-Detection-and-Alignment.git
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-07-14
- **Last Updated**: 2024-06-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Joint-Face-Detection-and-Alignment
==================================

Caffe and Python implementation of Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks.

## Set up

Set up environment and copy C++ layer code to Caffe's source code tree.

```
$ export PYTHONPATH=/path/to/Joint-Face-Detection-and-Alignment:$PYTHONPATH
$ export CAFFE_HOME=/path/to/caffe
$ sh layers/copy.sh
```

Compile Caffe following its document.

## Prepare data

Download dataset [WIDER](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/), [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) and [FDDB](http://vis-www.cs.umass.edu/fddb/). Put them in data directory like below.

```
data
├── CelebA
│   └── img_celeba
├── fddb
│   ├── FDDB-folds
│   ├── images
│   │   ├── 2002
│   │   └── 2003
│   └── result
│       └── images
└── WIDER
    ├── wider_face_split
    ├── WIDER_test
    ├── WIDER_train
    └── WIDER_val
```

I have write a matlab script to extract WIDER FACE info from matlab `mat` file to `txt` file.

## Train

Prepare data and train network follow the commands in `train.sh`.

## Test

Test the model with `demo.py` for simple detection and `fddb.py` for FDDB benchmark.

## Memory Issue

Since `pNet` may output many bboxes for `rNet` and Caffe's `Blob` never realloc the memory if your new data is smaller, this makes `Blob` only grow the memory and never reduce, which looks like a memory leak. It is fine for most cases but not for our case. You may modify `src/caffe/blob.cpp` if you encounter the memory issue.

```c++
template <typename Dtype>
void Blob<Dtype>::Reshape(const vector<int>& shape) {
  /* some code */
  if (count_ > capacity_) {  // never reduce the memory here
    capacity_ = count_;
    data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
    diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
  }
}
```

```c++
template <typename Dtype>
void Blob<Dtype>::Reshape(const vector<int>& shape) {
  /* some code */
  if (count_ != capacity_) {  // make a new data buffer
    capacity_ = count_;
    data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
    diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
  }
}
```

## References

- [A Convolutional Neural Network Cascade for Face Detection](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Li_A_Convolutional_Neural_2015_CVPR_paper.pdf)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](http://arxiv.org/abs/1604.02878)
- [MTCNN_face_detection_alignment](https://github.com/kpzhang93/MTCNN_face_detection_alignment)
- [py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn)