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@ -84,28 +84,6 @@ print(obj["age"]," years old ",obj["dominant_race"]," ",obj["dominant_emotion"],
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-2.jpg" width="95%" height="95%"></p>
**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. OpenCV is the default detector if you won't pass a 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/).
**Streaming and Real Time Analysis** - [`Demo`](https://youtu.be/-c9sSJcx6wI)
You can run deepface for real time videos as well.
@ -131,6 +109,28 @@ user
│ │ ├── 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. OpenCV is the default detector if you won't pass a 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)
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.