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@ -64,6 +64,25 @@ df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-6-v2.jpg" width="95%" height="95%"></p>
**Embeddings**
Face recognition models basically represent facial images as multi-dimensional vectors. Sometimes, you need those embedding vectors directly. DeepFace comes with a dedicated representation function.
```python
embedding = DeepFace.represent(img_path = "img.jpg")
```
This function returns an array as output. The size of the output array would be different based on the model name. For instance, VGG-Face is the default model for deepface and it represents facial images as 2622 dimensional vectors.
```python
assert isinstance(embedding, list)
assert len(embedding) == 2622
```
Here, embedding is also plotted with 2622 slots horizontally. Each slot is corresponding to a dimension value in the embedding vector and dimension value is explained in the colorbar on the right.
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/embedding.jpg" width="95%" height="95%"></p>
**Face recognition models** - [`Demo`](https://youtu.be/i_MOwvhbLdI)
Deepface is a **hybrid** face recognition package. It currently wraps many **state-of-the-art** face recognition models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Google FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/) and `SFace`. The default configuration uses VGG-Face model.
@ -76,6 +95,9 @@ result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", model_n
#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = models[1])
#embeddings
embedding = DeepFace.represent(img_path = "img.jpg", model_name = models[1])
```
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/model-portfolio-v8.jpg" width="95%" height="95%"></p>
@ -94,25 +116,6 @@ FaceNet, VGG-Face, ArcFace and Dlib are [overperforming](https://youtu.be/i_MOwv
| OpenFace | 93.80% | - |
| DeepID | - | 97.05% |
**Embeddings**
Face recognition models basically represent facial images as multi-dimensional vectors. Sometimes, you need those embedding vectors directly. DeepFace comes with a dedicated representation function.
```python
embedding = DeepFace.represent(img_path = "img.jpg", model_name = 'VGG-Face')
```
This function returns an array as output. The size of the output array would be different based on the model name. For instance, VGG-Face represents facial images as 2622 dimensional vectors.
```python
assert isinstance(embedding, list)
assert len(embedding) == 2622
```
Here, embedding is also plotted with 2622 slots horizontally. Each slot is corresponding to a dimension value in the embedding vector and dimension value is explained in the colorbar on the right.
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/embedding.jpg" width="95%" height="95%"></p>
**Similarity**
Face recognition models are regular [convolutional neural networks](https://sefiks.com/2018/03/23/convolutional-autoencoder-clustering-images-with-neural-networks/) and they are responsible to represent faces as vectors. We expect that a face pair of same person should be [more similar](https://sefiks.com/2020/05/22/fine-tuning-the-threshold-in-face-recognition/) than a face pair of different persons.