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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 ```python
models = ["VGG-Face", "Facenet", "OpenFace", "DeepFace"] models = ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
for model in models: 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. 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 ```python
metrics = ["cosine", "euclidean", "euclidean_l2"] metrics = ["cosine", "euclidean", "euclidean_l2"]
for metric in metrics: 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 **Ensemble learning for face recognition** - Demo