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@ -115,28 +115,6 @@ user
│ │ ├── Bob.jpg │ │ ├── Bob.jpg
``` ```
**Face Detectors** - [`Demo`](https://youtu.be/GZ2p2hj2H5k)
Face detection and alignment are early stages of a modern face recognition pipeline. [OpenCV haar cascade](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [SSD](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [Dlib](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/) and [MTCNN](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) methods are wrapped in deepface as a detector. You can optionally pass a custom detector to functions in deepface interface. MTCNN is the default detector if you won't pass any detector.
```python
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
for backend in backends:
#face detection and alignment
detected_face = DeepFace.detectFace("img.jpg", detector_backend = backend)
#face verification
obj = DeepFace.verify("img1.jpg", "img2.jpg", detector_backend = backend)
#face recognition
df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backend)
#facial analysis
demography = DeepFace.analyze("img4.jpg", detector_backend = backend)
```
[MTCNN](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) seems to overperform in detection and alignment stages but it is slower than [SSD](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/).
**Ensemble learning for face recognition** - [`Demo`](https://youtu.be/EIBJJJ0ECXU) **Ensemble learning for face recognition** - [`Demo`](https://youtu.be/EIBJJJ0ECXU)
A face recognition task can be handled by several models and similarity metrics. Herein, deepface offers a [special boosting and combination solution](https://sefiks.com/2020/06/03/mastering-face-recognition-with-ensemble-learning/) to improve the accuracy of a face recognition task. This provides a huge improvement on accuracy metrics. Human beings could have 97.53% score for face recognition tasks whereas this ensemble method passes the human level accuracy and gets 98.57% accuracy. On the other hand, this runs much slower than single models. A face recognition task can be handled by several models and similarity metrics. Herein, deepface offers a [special boosting and combination solution](https://sefiks.com/2020/06/03/mastering-face-recognition-with-ensemble-learning/) to improve the accuracy of a face recognition task. This provides a huge improvement on accuracy metrics. Human beings could have 97.53% score for face recognition tasks whereas this ensemble method passes the human level accuracy and gets 98.57% accuracy. On the other hand, this runs much slower than single models.
@ -160,6 +138,28 @@ python api.py
The both face recognition and facial attribute analysis are covered in the API. You are expected to call these functions as http post methods. Service endpoints will be `http://127.0.0.1:5000/verify` for face recognition and `http://127.0.0.1:5000/analyze` for facial attribute analysis. You should pass input images as base64 encoded string in this case. [Here](https://github.com/serengil/deepface/tree/master/api), you can find a postman project. The both face recognition and facial attribute analysis are covered in the API. You are expected to call these functions as http post methods. Service endpoints will be `http://127.0.0.1:5000/verify` for face recognition and `http://127.0.0.1:5000/analyze` for facial attribute analysis. You should pass input images as base64 encoded string in this case. [Here](https://github.com/serengil/deepface/tree/master/api), you can find a postman project.
**Face Detectors** - [`Demo`](https://youtu.be/GZ2p2hj2H5k)
Face detection and alignment are early stages of a modern face recognition pipeline. [OpenCV haar cascade](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [SSD](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [Dlib](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/) and [MTCNN](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) methods are wrapped in deepface as a detector. You can optionally pass a custom detector to functions in deepface interface. MTCNN is the default detector if you won't pass any detector.
```python
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
for backend in backends:
#face detection and alignment
detected_face = DeepFace.detectFace("img.jpg", detector_backend = backend)
#face verification
obj = DeepFace.verify("img1.jpg", "img2.jpg", detector_backend = backend)
#face recognition
df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backend)
#facial analysis
demography = DeepFace.analyze("img4.jpg", detector_backend = backend)
```
[MTCNN](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) seems to overperform in detection and alignment stages but it is slower than [SSD](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/).
**Passing pre-built face recognition models** **Passing pre-built face recognition models**
You can build models once and pass to deepface functions as well. This speeds you up if you are going to call deepface several times. You can build models once and pass to deepface functions as well. This speeds you up if you are going to call deepface several times.