github/deepface
1
1
Fork 0
mirror of https://github.com/serengil/deepface.git synced 2025-06-07 20:15:21 +00:00
Code Issues Packages Projects Releases Wiki Activity

aim of the framework

Browse Source
This commit is contained in:
Sefik Ilkin Serengil 2020-03-26 16:37:10 +03:00 committed by GitHub
parent 3e06ac1a9b
commit 9c779be300
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 3 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
README.md
View File

@ -4,7 +4,7 @@
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-icon-labeled.png" width="200" height="240"></p> <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-icon-labeled.png" width="200" height="240"></p>
**Deepface** is a lightweight facial analysis framework including face recognition and demography (age, gender, emotion and race) for Python. Modern face recognition pipelines consist of 4 stages: detect, [align](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), represent and verify. Deepface handles all these common stages in the background. You can use the framework with a just few lines of codes **Deepface** is a lightweight facial analysis framework including face recognition and demography (age, gender, emotion and race) for Python. You can apply facial analysis with a few lines of code. It plans to bridge a gap between software engineering and machine learning studies.
# Face Recognition # Face Recognition
@ -19,6 +19,8 @@ print("Is verified: ", result["verified"])
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/tests/dataset/test-case-6.jpg" width="95%" height="95%"></p> <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/tests/dataset/test-case-6.jpg" width="95%" height="95%"></p>
Modern face recognition pipelines consist of 4 stages: detect, [align](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), represent and verify. Deepface handles all these common stages in the background. You can use the framework with a just few lines of codes.
Each call of verification function builds a face recognition model from scratch and this is a costly operation. If you are going to verify multiple faces sequentially, then you should pass an array of faces to verification function to speed the operation up. In this way, complex face recognition models will be built once. Each call of verification function builds a face recognition model from scratch and this is a costly operation. If you are going to verify multiple faces sequentially, then you should pass an array of faces to verification function to speed the operation up. In this way, complex face recognition models will be built once.
```python ```python