# 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.jpg'.

*Write a pipeline with explicit inputs/outputs name specifications:*
```python
from towhee 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"/>

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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).


# More Resources

- [Exploring Multimodal Embeddings with FiftyOne and Milvus - Zilliz blog](https://zilliz.com/blog/exploring-multimodal-embeddings-with-fiftyone-and-milvus): This post explored how multimodal embeddings work with Voxel51 and Milvus.
- [How to Get the Right Vector Embeddings - Zilliz blog](https://zilliz.com/blog/how-to-get-the-right-vector-embeddings): A comprehensive introduction to vector embeddings and how to generate them with popular open-source models.
- [The guide to clip-vit-base-patch32 | OpenAI](https://zilliz.com/ai-models/clip-vit-base-patch32): clip-vit-base-patch32: a CLIP multimodal model variant by OpenAI for image and text embedding.
- [Using Vector Search to Better Understand Computer Vision Data - Zilliz blog](https://zilliz.com/blog/use-vector-search-to-better-understand-computer-vision-data): How Vector Search improves your understanding of Computer Vision Data
- [Sparse and Dense Embeddings - Zilliz blog](https://zilliz.com/learn/sparse-and-dense-embeddings): Learn about sparse and dense embeddings, their use cases, and a text classification example using these embeddings.
- [Understanding Neural Network Embeddings - Zilliz blog](https://zilliz.com/learn/understanding-neural-network-embeddings): This article is dedicated to going a bit more in-depth into embeddings/embedding vectors, along with how they are used in modern ML algorithms and pipelines.
- [Enhancing Information Retrieval with Sparse Embeddings | Zilliz Learn - Zilliz blog](https://zilliz.com/learn/enhancing-information-retrieval-learned-sparse-embeddings): Explore the inner workings, advantages, and practical applications of learned sparse embeddings with the Milvus vector database
- [An Introduction to Vector Embeddings: What They Are and How to Use Them - Zilliz blog](https://zilliz.com/learn/everything-you-should-know-about-vector-embeddings): In this blog post, we will understand the concept of vector embeddings and explore its applications, best practices, and tools for working with embeddings.