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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** - [`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 ```python
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db") 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> <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-6-v2.jpg" width="95%" height="95%"></p>
**Embeddings** **Embeddings**
@ -82,7 +77,7 @@ This function returns an array as output. The size of the output array would be
```python ```python
assert isinstance(embedding, list) 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. 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.