# Face Embeddings using Deepface
  *Author: Krishna katyal*

<br />



## 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).


<br />


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

*Write a pipeline with explicit inputs/outputs name specifications:*
from towhee.dc2 import pipe, ops, DataCollection

p = (
    pipe.input('path')
        .map('path', 'img', ops.image_decode())
        .map('img', 'vec', ops.face_embedding.deepface(model_name = 'DeepFace')))
        .output('img', 'vec')
)

DataCollection(p('./test_face.jpg')).show()

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

<br />


## 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


<br />



## 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.


<br />




**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).