docstring updated for ghostface

README.md

@@ -21,7 +21,7 @@
 
 <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. 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/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/) and `SFace`.
+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/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/), `SFace` and `GhostFaceNet`.
 
 Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.
 
@@ -100,7 +100,7 @@ Here, embedding is also [plotted](https://sefiks.com/2020/05/01/a-gentle-introdu
 
 **Face recognition models** - [`Demo`](https://youtu.be/i_MOwvhbLdI)
 
-Deepface is a **hybrid** face recognition package. It currently wraps many **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/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/) and `SFace`. The default configuration uses VGG-Face model.
+Deepface is a **hybrid** face recognition package. It currently wraps many **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/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/), `SFace` and `GhostFaceNet`. The default configuration uses VGG-Face model.
 
 ```python
 models = [
@@ -113,6 +113,7 @@ models = [
   "ArcFace", 
   "Dlib", 
   "SFace",
+  "GhostFaceNet",
 ]
 
 #face verification
@@ -135,19 +136,22 @@ embedding_objs = DeepFace.represent(img_path = "img.jpg",
 
 <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/model-portfolio-v8.jpg" width="95%" height="95%"></p>
 
-FaceNet, VGG-Face, ArcFace and Dlib are [overperforming](https://youtu.be/i_MOwvhbLdI) ones based on experiments. You can find out the scores of those models below on both [Labeled Faces in the Wild](https://sefiks.com/2020/08/27/labeled-faces-in-the-wild-for-face-recognition/) and YouTube Faces in the Wild data sets declared by its creators.
+FaceNet, VGG-Face, ArcFace and Dlib are [overperforming](https://youtu.be/i_MOwvhbLdI) ones based on experiments. You can find out the scores of those models below on [Labeled Faces in the Wild](https://sefiks.com/2020/08/27/labeled-faces-in-the-wild-for-face-recognition/) set declared by its creators. 
 
-| Model | LFW Score | YTF Score |
-| ---   | --- | --- |
-| Facenet512 | 99.65% | - |
-| SFace | 99.60% | - |
-| ArcFace | 99.41% | - |
-| Dlib | 99.38 % | - |
-| Facenet | 99.20% | - |
-| VGG-Face | 98.78% | 97.40% |
-| *Human-beings* | *97.53%* | - |
-| OpenFace | 93.80% | - |
-| DeepID | - | 97.05% |
+| Model | Declared LFW Score |
+| ---   | --- |
+| VGG-Face | 98.78% |
+| Facenet | 99.20% |
+| Facenet512 | 99.65% |
+| OpenFace | 93.80% |
+| DeepID | - |
+| Dlib | 99.38 % |
+| SFace | 99.60% |
+| ArcFace | 99.41% |
+| GhostFaceNet | 99.76 |
+| *Human-beings* | *97.53%* |
+
+Conducting experiments with those models within DeepFace may reveal disparities compared to the original studies, owing to the adoption of distinct detection or normalization techniques. Furthermore, some models have been released solely with their backbones, lacking pre-trained weights. Thus, we are utilizing their re-implementations instead of the original pre-trained weights.
 
 **Similarity**
 
@@ -374,6 +378,6 @@ Also, if you use deepface in your GitHub projects, please add `deepface` in the
 
 DeepFace is licensed under the MIT License - see [`LICENSE`](https://github.com/serengil/deepface/blob/master/LICENSE) for more details.
 
-DeepFace wraps some external face recognition models: [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/), [Facenet](https://github.com/davidsandberg/facenet/blob/master/LICENSE.md), [OpenFace](https://github.com/iwantooxxoox/Keras-OpenFace/blob/master/LICENSE), [DeepFace](https://github.com/swghosh/DeepFace), [DeepID](https://github.com/Ruoyiran/DeepID/blob/master/LICENSE.md), [ArcFace](https://github.com/leondgarse/Keras_insightface/blob/master/LICENSE), [Dlib](https://github.com/davisking/dlib/blob/master/dlib/LICENSE.txt), and [SFace](https://github.com/opencv/opencv_zoo/blob/master/models/face_recognition_sface/LICENSE). Besides, age, gender and race / ethnicity models were trained on the backbone of VGG-Face with transfer learning. Licence types will be inherited if you are going to use those models. Please check the license types of those models for production purposes.
+DeepFace wraps some external face recognition models: [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/), [Facenet](https://github.com/davidsandberg/facenet/blob/master/LICENSE.md), [OpenFace](https://github.com/iwantooxxoox/Keras-OpenFace/blob/master/LICENSE), [DeepFace](https://github.com/swghosh/DeepFace), [DeepID](https://github.com/Ruoyiran/DeepID/blob/master/LICENSE.md), [ArcFace](https://github.com/leondgarse/Keras_insightface/blob/master/LICENSE), [Dlib](https://github.com/davisking/dlib/blob/master/dlib/LICENSE.txt), [SFace](https://github.com/opencv/opencv_zoo/blob/master/models/face_recognition_sface/LICENSE) and [`GhostFaceNet`](https://github.com/HamadYA/GhostFaceNets/blob/main/LICENSE). Besides, age, gender and race / ethnicity models were trained on the backbone of VGG-Face with transfer learning. Licence types will be inherited if you are going to use those models. Please check the license types of those models for production purposes.
 
 DeepFace [logo](https://thenounproject.com/term/face-recognition/2965879/) is created by [Adrien Coquet](https://thenounproject.com/coquet_adrien/) and it is licensed under [Creative Commons: By Attribution 3.0 License](https://creativecommons.org/licenses/by/3.0/).

deepface/DeepFace.py

@@ -76,7 +76,7 @@ def verify(
             or pre-calculated embeddings.
 
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
-            OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face).
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
             'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv).
@@ -254,7 +254,7 @@ def find(
             in the database will be considered in the decision-making process.
 
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
-            OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face).
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
             'euclidean', 'euclidean_l2' (default is cosine).
@@ -331,7 +331,8 @@ def represent(
             include information for each detected face.
 
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
-            OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face.).
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet
+            (default is VGG-Face.).
 
         enforce_detection (boolean): If no face is detected in an image, raise an exception.
             Default is True. Set to False to avoid the exception for low-resolution images
@@ -393,7 +394,7 @@ def stream(
             in the database will be considered in the decision-making process.
 
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
-            OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face).
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
             'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv).

deepface/modules/recognition.py

@@ -43,7 +43,7 @@ def find(
             in the database will be considered in the decision-making process.
 
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
-            OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
             'euclidean', 'euclidean_l2'.
@@ -319,7 +319,8 @@ def __find_bulk_embeddings(
     Args:
         employees (list): list of exact image paths
 
-        model_name (str): facial recognition model name
+        model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
         target_size (tuple): expected input shape of facial recognition model
 

deepface/modules/representation.py

@@ -28,7 +28,7 @@ def represent(
             include information for each detected face.
 
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
-            OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet
 
         enforce_detection (boolean): If no face is detected in an image, raise an exception.
             Default is True. Set to False to avoid the exception for low-resolution images.

deepface/modules/streaming.py

@@ -41,7 +41,7 @@ def analysis(
             in the database will be considered in the decision-making process.
 
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
-            OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face).
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
             'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv).
@@ -161,7 +161,7 @@ def build_facial_recognition_model(model_name: str) -> tuple:
     Build facial recognition model
     Args:
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
-            OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face).
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
     Returns
         input_shape (tuple): input shape of given facial recognitio n model.
     """
@@ -184,7 +184,7 @@ def search_identity(
         db_path (string): Path to the folder containing image files. All detected faces
             in the database will be considered in the decision-making process.
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
-            OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face).
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
             'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv).
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
@@ -424,7 +424,7 @@ def perform_facial_recognition(
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
             'euclidean', 'euclidean_l2' (default is cosine).
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
-            OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face).
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
     Returns:
         img (np.ndarray): image with identified face informations
     """

deepface/modules/verification.py

@@ -42,7 +42,7 @@ def verify(
             or pre-calculated embeddings.
 
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
-            OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face).
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
             'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv)
@@ -343,7 +343,8 @@ def find_threshold(model_name: str, distance_metric: str) -> float:
     """
     Retrieve pre-tuned threshold values for a model and distance metric pair
     Args:
-        model_name (str): facial recognition model name
+        model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
         distance_metric (str): distance metric name. Options are cosine, euclidean
             and euclidean_l2.
     Returns:
@@ -368,6 +369,7 @@ def find_threshold(model_name: str, distance_metric: str) -> float:
         "OpenFace": {"cosine": 0.10, "euclidean": 0.55, "euclidean_l2": 0.55},
         "DeepFace": {"cosine": 0.23, "euclidean": 64, "euclidean_l2": 0.64},
         "DeepID": {"cosine": 0.015, "euclidean": 45, "euclidean_l2": 0.17},
+        "GhostFaceNet": {"cosine": 0.65, "euclidean": 35.71, "euclidean_l2": 1.10},
     }
 
     threshold = thresholds.get(model_name, base_threshold).get(distance_metric, 0.4)