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@ -54,9 +54,8 @@ Deepface is a **hybrid** face recognition package. It currently wraps the **stat
```python ```python
models = ["VGG-Face", "Facenet", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib"] models = ["VGG-Face", "Facenet", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib"]
for model in models: result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = models[1])
result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = model) df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = models[1])
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = model)
``` ```
FaceNet, VGG-Face, ArcFace and Dlib [overperforms](https://youtu.be/i_MOwvhbLdI) than OpenFace, DeepFace and DeepID based on experiments. Supportively, FaceNet got 99.65%; ArcFace got 99.40%; Dlib got 99.38%; VGG-Face got 98.78%; OpenFace got 93.80% accuracy scores on [LFW data set](https://sefiks.com/2020/08/27/labeled-faces-in-the-wild-for-face-recognition/) whereas human beings could have just 97.53%. FaceNet, VGG-Face, ArcFace and Dlib [overperforms](https://youtu.be/i_MOwvhbLdI) than OpenFace, DeepFace and DeepID based on experiments. Supportively, FaceNet got 99.65%; ArcFace got 99.40%; Dlib got 99.38%; VGG-Face got 98.78%; OpenFace got 93.80% accuracy scores on [LFW data set](https://sefiks.com/2020/08/27/labeled-faces-in-the-wild-for-face-recognition/) whereas human beings could have just 97.53%.
@ -69,9 +68,8 @@ Distance could be found by different metrics such as [Cosine Similarity](https:/
```python ```python
metrics = ["cosine", "euclidean", "euclidean_l2"] metrics = ["cosine", "euclidean", "euclidean_l2"]
for metric in metrics: result = DeepFace.verify("img1.jpg", "img2.jpg", distance_metric = metrics[1])
result = DeepFace.verify("img1.jpg", "img2.jpg", distance_metric = metric) df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", distance_metric = metrics[1])
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", distance_metric = metric)
``` ```
Euclidean L2 form [seems](https://youtu.be/i_MOwvhbLdI) to be more stable than cosine and regular Euclidean distance based on experiments. Euclidean L2 form [seems](https://youtu.be/i_MOwvhbLdI) to be more stable than cosine and regular Euclidean distance based on experiments.
@ -81,7 +79,6 @@ Euclidean L2 form [seems](https://youtu.be/i_MOwvhbLdI) to be more stable than c
Deepface also offers facial attribute analysis including [`age`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`gender`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`facial expression`](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) (including angry, fear, neutral, sad, disgust, happy and surprise) and [`race`](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/) (including asian, white, middle eastern, indian, latino and black) predictions. Analysis function under the DeepFace interface is used to find demography of a face. Deepface also offers facial attribute analysis including [`age`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`gender`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`facial expression`](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) (including angry, fear, neutral, sad, disgust, happy and surprise) and [`race`](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/) (including asian, white, middle eastern, indian, latino and black) predictions. Analysis function under the DeepFace interface is used to find demography of a face.
```python ```python
from deepface import DeepFace
obj = DeepFace.analyze(img_path = "img4.jpg", actions = ['age', 'gender', 'race', 'emotion']) obj = DeepFace.analyze(img_path = "img4.jpg", actions = ['age', 'gender', 'race', 'emotion'])
``` ```
@ -122,18 +119,18 @@ Face detection and alignment are early stages of a modern face recognition pipel
```python ```python
backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface'] backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface']
for backend in backends:
#face detection and alignment
detected_face = DeepFace.detectFace("img.jpg", detector_backend = backend)
#face verification #face detection and alignment
obj = DeepFace.verify("img1.jpg", "img2.jpg", detector_backend = backend) detected_face = DeepFace.detectFace("img.jpg", detector_backend = backends[4])
#face recognition #face verification
df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backend) obj = DeepFace.verify("img1.jpg", "img2.jpg", detector_backend = backends[4])
#facial analysis #face recognition
demography = DeepFace.analyze("img4.jpg", detector_backend = backend) df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backends[4])
#facial analysis
demography = DeepFace.analyze("img4.jpg", detector_backend = backends[4])
``` ```
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-detectors.png" width="90%" height="90%"></p> <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-detectors.png" width="90%" height="90%"></p>