github/deepface
1
1
Fork 0
mirror of https://github.com/serengil/deepface.git synced 2025-06-08 20:45:22 +00:00
Code Issues Packages Projects Releases Wiki Activity

models and metrics

Browse Source
This commit is contained in:
Sefik Ilkin Serengil 2020-06-10 09:28:23 +03:00 committed by GitHub
parent 1333934b7d
commit 1882e169bb
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 6 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
README.md
View File

@ -62,11 +62,9 @@ print(df.head())
Face recognition can be handled by different models. Currently, [`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/) and [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-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.
```python
vggface_result = DeepFace.verify("img1.jpg", "img2.jpg") #default is VGG-Face
#vggface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face") #identical to the line above
facenet_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "Facenet")
openface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "OpenFace")
deepface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "DeepFace")
models = ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
for model in models:
result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = model)
```
The complexity and response time of each face recognition model is different so do accuracy scores. Mean ± std. dev. of 7 runs on CPU for each model in my experiments is illustrated in the following table.
@ -93,9 +91,9 @@ Face recognition models are regular [convolutional neural networks](https://sefi
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.
```python
result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distance_metric = "cosine")
result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distance_metric = "euclidean")
result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distance_metric = "euclidean_l2")
metrics = ["cosine", "euclidean", "euclidean_l2"]
for metric in metrics:
result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distance_metric = metric)
```
**Ensemble learning for face recognition** - Demo