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@ -56,12 +56,18 @@ result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")
**Face recognition** - [`Demo`](https://youtu.be/Hrjp-EStM_s)
[Face recognition](https://sefiks.com/2020/05/25/large-scale-face-recognition-for-deep-learning/) requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return pandas data frame as output.
[Face recognition](https://sefiks.com/2020/05/25/large-scale-face-recognition-for-deep-learning/) requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action.
```python
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")
```
It's going to look for the identity of input image in the database path and it will return pandas data frame as output.
```python
assert isinstance(df, pd.DataFrame)
```
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-6-v2.jpg" width="95%" height="95%"></p>
**Embeddings**
@ -72,7 +78,14 @@ Face recognition models basically represent facial images as multi-dimensional v
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. 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.
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 model_name = "VGG-Face" and 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>