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@ -56,18 +56,13 @@ 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.
[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.
```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**
@ -82,7 +77,7 @@ This function returns an array as output. The size of the output array would be
```python
assert isinstance(embedding, list)
assert model_name = "VGG-Face" and len(embedding) = 2622
assert (model_name = "VGG-Face" and len(embedding) == 2622) or (model_name = "Facenet" and len(embedding) == 128)
```
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.