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README.md
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@ -1,10 +1,11 @@
# deepface # deepface
[![Downloads](https://pepy.tech/badge/deepface)](https://pepy.tech/project/deepface) [![Downloads](https://pepy.tech/badge/deepface)](https://pepy.tech/project/deepface)
[![License](http://img.shields.io/:license-MIT-blue.svg?style=flat)](https://github.com/serengil/deepface/blob/master/LICENSE)
<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 [face recognition](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and facial attribute analysis ([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/), [emotion](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) and [race](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/)) framework for python. You can apply facial analysis with just a few lines of code. It plans to bridge a gap between software engineering and machine learning studies. **deepface** is a lightweight [face recognition](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and facial attribute analysis ([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/), [emotion](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) and [race](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/)) framework for python. It is a hybrid face recognition framework wrapping **state-of-the-art** models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Google FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/) and [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/).
## Installation ## Installation
@ -16,7 +17,7 @@ pip install deepface
## Face Recognition ## Face Recognition
A modern [**face recognition pipeline**](https://sefiks.com/2020/05/01/a-gentle-introduction-to-face-recognition-in-deep-learning/) consists of 4 common stages: [detect](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [align](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [represent](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and [verify](https://sefiks.com/2020/05/22/fine-tuning-the-threshold-in-face-recognition/). **DeepFace** handles all these common stages in the background. A modern [**face recognition pipeline**](https://sefiks.com/2020/05/01/a-gentle-introduction-to-face-recognition-in-deep-learning/) consists of 4 common stages: [detect](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [align](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [represent](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and [verify](https://sefiks.com/2020/05/22/fine-tuning-the-threshold-in-face-recognition/). **deepface** handles all these common stages in the background.
**Face Verification** - [`Demo`](https://youtu.be/KRCvkNCOphE) **Face Verification** - [`Demo`](https://youtu.be/KRCvkNCOphE)
@ -44,7 +45,7 @@ df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-6-v2.jpg" width="95%" height="95%"></p> <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-6-v2.jpg" width="95%" height="95%"></p>
**Face recognition models** **Face recognition models** - [`Demo`](https://youtu.be/i_MOwvhbLdI)
Deepface is a hybrid face recognition package. It currently wraps the **state-of-the-art** face recognition models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Google FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/) and [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/). The default configuration verifies faces with **VGG-Face** model. You can set the base model while verification as illustared below. Deepface is a hybrid face recognition package. It currently wraps the **state-of-the-art** face recognition models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Google FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/) and [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/). The default configuration verifies faces with **VGG-Face** model. You can set the base model while verification as illustared below.
@ -54,7 +55,7 @@ for model in models:
result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = model) result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = model)
``` ```
FaceNet and VGG-Face [overperforms](https://youtu.be/i_MOwvhbLdI) than OpenFace, DeepFace and DeepID based on experiments. FaceNet, VGG-Face and Dlib [overperforms](https://youtu.be/i_MOwvhbLdI) than OpenFace, DeepFace and DeepID based on experiments.
**Similarity** **Similarity**
@ -167,7 +168,7 @@ You can also read a translated version of deepface tutorials in [Chinese (深臉
Reference face recognition models have different type of licenses. This framework is just a wrapper for those models. That's why, licence types are inherited as well. You should check the licenses for the face recognition models before use. Reference face recognition models have different type of licenses. This framework is just a wrapper for those models. That's why, licence types are inherited as well. You should check the licenses for the face recognition models before use.
Herein, [OpenFace](https://github.com/cmusatyalab/openface/blob/master/LICENSE) is licensed under Apache License 2.0. [FB DeepFace](https://github.com/swghosh/DeepFace) and [Facenet](https://github.com/davidsandberg/facenet/blob/master/LICENSE.md) is licensed under MIT License. The both Apache License 2.0 and MIT license types allow you to use for commercial purpose. Herein, [OpenFace](https://github.com/cmusatyalab/openface/blob/master/LICENSE) is licensed under Apache License 2.0. [FB DeepFace](https://github.com/swghosh/DeepFace) and [Facenet](https://github.com/davidsandberg/facenet/blob/master/LICENSE.md) is licensed under MIT License. [Dlib](https://github.com/davisking/dlib/blob/master/dlib/LICENSE.txt) is licensed under Boost Software License. They all allow you to use for personal and commercial purpose free.
On the other hand, [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/) is licensed under Creative Commons Attribution License. That's why, it is restricted to adopt VGG-Face for commercial use. On the other hand, [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/) is licensed under Creative Commons Attribution License. That's why, it is restricted to adopt VGG-Face for commercial use.

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deepface/basemodels/DeepID.py
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@ -8,7 +8,7 @@ import zipfile
#------------------------------------- #-------------------------------------
def loadModel(): def loadModel(url = 'https://drive.google.com/uc?id=1uRLtBCTQQAvHJ_KVrdbRJiCKxU8m5q2J'):
myInput = Input(shape=(55, 47, 3)) myInput = Input(shape=(55, 47, 3))
@ -43,7 +43,6 @@ def loadModel():
if os.path.isfile(home+'/.deepface/weights/deepid_keras_weights.h5') != True: if os.path.isfile(home+'/.deepface/weights/deepid_keras_weights.h5') != True:
print("deepid_keras_weights.h5 will be downloaded...") print("deepid_keras_weights.h5 will be downloaded...")
url = 'https://drive.google.com/uc?id=1uRLtBCTQQAvHJ_KVrdbRJiCKxU8m5q2J'
output = home+'/.deepface/weights/deepid_keras_weights.h5' output = home+'/.deepface/weights/deepid_keras_weights.h5'
gdown.download(url, output, quiet=False) gdown.download(url, output, quiet=False)

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deepface/basemodels/Facenet.py
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@ -530,7 +530,7 @@ def InceptionResNetV2():
return model return model
def loadModel(): def loadModel(url = 'https://drive.google.com/uc?id=1971Xk5RwedbudGgTIrGAL4F7Aifu7id1'):
model = InceptionResNetV2() model = InceptionResNetV2()
#----------------------------------- #-----------------------------------
@ -540,7 +540,6 @@ def loadModel():
if os.path.isfile(home+'/.deepface/weights/facenet_weights.h5') != True: if os.path.isfile(home+'/.deepface/weights/facenet_weights.h5') != True:
print("facenet_weights.h5 will be downloaded...") print("facenet_weights.h5 will be downloaded...")
url = 'https://drive.google.com/uc?id=1971Xk5RwedbudGgTIrGAL4F7Aifu7id1'
output = home+'/.deepface/weights/facenet_weights.h5' output = home+'/.deepface/weights/facenet_weights.h5'
gdown.download(url, output, quiet=False) gdown.download(url, output, quiet=False)

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deepface/basemodels/FbDeepFace.py
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@ -8,7 +8,7 @@ import zipfile
#------------------------------------- #-------------------------------------
def loadModel(): def loadModel(url = 'https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip'):
base_model = Sequential() base_model = Sequential()
base_model.add(Convolution2D(32, (11, 11), activation='relu', name='C1', input_shape=(152, 152, 3))) base_model.add(Convolution2D(32, (11, 11), activation='relu', name='C1', input_shape=(152, 152, 3)))
base_model.add(MaxPooling2D(pool_size=3, strides=2, padding='same', name='M2')) base_model.add(MaxPooling2D(pool_size=3, strides=2, padding='same', name='M2'))
@ -28,8 +28,6 @@ def loadModel():
if os.path.isfile(home+'/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5') != True: if os.path.isfile(home+'/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5') != True:
print("VGGFace2_DeepFace_weights_val-0.9034.h5 will be downloaded...") print("VGGFace2_DeepFace_weights_val-0.9034.h5 will be downloaded...")
url = 'https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip'
output = home+'/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5.zip' output = home+'/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5.zip'
gdown.download(url, output, quiet=False) gdown.download(url, output, quiet=False)

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deepface/basemodels/OpenFace.py
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@ -15,7 +15,7 @@ from keras import backend as K
#--------------------------------------- #---------------------------------------
def loadModel(): def loadModel(url = 'https://drive.google.com/uc?id=1LSe1YCV1x-BfNnfb7DFZTNpv_Q9jITxn'):
myInput = Input(shape=(96, 96, 3)) myInput = Input(shape=(96, 96, 3))
x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput) x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
@ -237,7 +237,6 @@ def loadModel():
if os.path.isfile(home+'/.deepface/weights/openface_weights.h5') != True: if os.path.isfile(home+'/.deepface/weights/openface_weights.h5') != True:
print("openface_weights.h5 will be downloaded...") print("openface_weights.h5 will be downloaded...")
url = 'https://drive.google.com/uc?id=1LSe1YCV1x-BfNnfb7DFZTNpv_Q9jITxn'
output = home+'/.deepface/weights/openface_weights.h5' output = home+'/.deepface/weights/openface_weights.h5'
gdown.download(url, output, quiet=False) gdown.download(url, output, quiet=False)

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deepface/basemodels/VGGFace.py
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@ -54,7 +54,7 @@ def baseModel():
return model return model
def loadModel(): def loadModel(url = 'https://drive.google.com/uc?id=1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo'):
model = baseModel() model = baseModel()
@ -63,9 +63,8 @@ def loadModel():
home = str(Path.home()) home = str(Path.home())
if os.path.isfile(home+'/.deepface/weights/vgg_face_weights.h5') != True: if os.path.isfile(home+'/.deepface/weights/vgg_face_weights.h5') != True:
print("vgg_face_weights.h5 will be downloaded...") print("vgg_face_weights.h5 will be downloaded...")
url = 'https://drive.google.com/uc?id=1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo'
output = home+'/.deepface/weights/vgg_face_weights.h5' output = home+'/.deepface/weights/vgg_face_weights.h5'
gdown.download(url, output, quiet=False) gdown.download(url, output, quiet=False)