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@ -20,7 +20,7 @@ A modern [face recognition pipeline](https://sefiks.com/2020/05/01/a-gentle-intr
**Face Verification** - [`Demo`](https://youtu.be/KRCvkNCOphE)
Verification function under the DeepFace interface offers a single face recognition.
Verification function under the DeepFace interface offers a single face recognition. Each call of the function builds a face recognition model and this is very costly. If you are going to verify several faces sequentially, then you should pass an array of faces to the function instead of calling the function in a for loop. In this way, complex face recognition models will be built once and this will speed the function up dramatically. Besides, calling the function in a for loop might cause memory problems as well.
```python
from deepface import DeepFace
@ -33,8 +33,6 @@ print(results)
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-1.jpg" width="95%" height="95%"></p>
Each call of the function builds a face recognition model and this is very costly. If you are going to verify several faces sequentially, then you should pass an array of faces to the function instead of calling the function in a for loop. In this way, complex face recognition models will be built once and this will speed the function up dramatically. Besides, calling the function in a for loop might cause memory problems as well.
**Large scale face recognition** - [`Demo`](https://youtu.be/Hrjp-EStM_s)
You can apply face recognition on a [large scale](https://sefiks.com/2020/05/25/large-scale-face-recognition-for-deep-learning/) data set as well. Face recognition requires to apply face verification multiple times. Herein, deepface offers an out-of-the-box find function to handle this action. Representations of faces photos in your database folder will be stored in a pickle file when find function is called once. Then, deepface just finds representation of the target image. In this way, finding an identity in a large scale data set will be performed in just seconds.