# Face Embeddings using Deepface
  *Author: Krishna katyal*

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## Description
The pipeline is used to extract the feature vector of detected faces in images. It uses the  for face embeddings   [`Deepface`](https://github.com/serengil/deepface).


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## Code Example
Load an image from path './test_face'. 

 *Write the pipeline in simplified style*:

```python
import towhee 
towhee.glob('./test_face.jpg') \
  .image_decode.cv2() \
  .face_embedding.deepface(model_name = 'DeepFace').to_list()
```


<img src="./image.png" height="200px"/>

*Write a pipeline with explicit inputs/outputs name specifications:*


```python
import towhee

towhee.glob['path']('./test_face.jpgg') \
  .image_decode.cv2['path', 'img']() \
  .face_embedding.deepface['img', 'vec']() \
  .select['img','vec']() \
  .show()
```

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## Factory Constructor

Create the operator via the following factory method

***face_embedding.deepface(model_name = 'which  model to use')***

Model options: 
- VGG-Face
- FaceNet
- OpenFace
- DeepFace
- ArcFace
- Dlib
- DeepID
- Facenet512


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## Interface

A face embedding operator takes a face image as input. It extracts the embedding in ndarray.

**Parameters:**

​	***img:*** *towhee.types.Image (a sub-class of numpy.ndarray)*

​	The input image.

**Returns:** *numpy.ndarray*

​	The extracted image embedding.


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**Reference**

https://github.com/serengil/deepface

You can refer to [Getting Started with Towhee](https://towhee.io/) for more details. If you have any questions, you can [submit an issue to the towhee repository](https://github.com/towhee-io/towhee/issues).