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Sefik Ilkin Serengil 2020-06-10 09:32:35 +03:00 committed by GitHub
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@ -60,7 +60,7 @@ Face recognition can be handled by different models. Currently, [`VGG-Face`](htt
```python
models = ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
for model in models:
result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = model)
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.
@ -89,7 +89,7 @@ Distance could be found by different metrics such as [Cosine Similarity](https:/
```python
metrics = ["cosine", "euclidean", "euclidean_l2"]
for metric in metrics:
result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distance_metric = metric)
result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distance_metric = metric)
```
**Ensemble learning for face recognition** - Demo