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@ -9,7 +9,7 @@ from deepface import DeepFace
result = DeepFace.verify("img1.jpg", "img2.jpg") result = DeepFace.verify("img1.jpg", "img2.jpg")
``` ```
# Face recognition models ## Face recognition models
Face recognition can be handled by different models. Currently, [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Facenet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/) and [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/) models are supported in deepface. The default configuration verifies faces with **VGG-Face** model. You can set the base model while verification as illustared below. Accuracy and speed show difference based on the performing model. Face recognition can be handled by different models. Currently, [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Facenet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/) and [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/) models are supported in deepface. The default configuration verifies faces with **VGG-Face** model. You can set the base model while verification as illustared below. Accuracy and speed show difference based on the performing model.
@ -20,7 +20,7 @@ facenet_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "Facenet")
openface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "OpenFace") openface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "OpenFace")
``` ```
# Similarity ## Similarity
These models actually find the vector embeddings of faces. Decision of verification is based on the distance between vectors. Distance could be found by different metrics such as [`Cosine Similarity`](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance and L2 form. The default configuration finds the **cosine similarity**. You can alternatively set the similarity metric while verification as demostratred below. These models actually find the vector embeddings of faces. Decision of verification is based on the distance between vectors. Distance could be found by different metrics such as [`Cosine Similarity`](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance and L2 form. The default configuration finds the **cosine similarity**. You can alternatively set the similarity metric while verification as demostratred below.
@ -32,7 +32,7 @@ result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distan
VGG-Face has the highest accuracy score but it is not convenient for real time studies because of its complex structure. Facenet is a complex model as well. On the other hand, OpenFace has a close accuracy score but it performs the fastest. That's why, OpenFace is much more convenient for real time studies. VGG-Face has the highest accuracy score but it is not convenient for real time studies because of its complex structure. Facenet is a complex model as well. On the other hand, OpenFace has a close accuracy score but it performs the fastest. That's why, OpenFace is much more convenient for real time studies.
# Verification ## Verification
Verification function returns a tuple including boolean verification result, distance between two faces and max threshold to identify. Verification function returns a tuple including boolean verification result, distance between two faces and max threshold to identify.