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@ -134,9 +134,11 @@ models = [
"ArcFace",
"Dlib",
"SFace",
"GhostFaceNet"
"GhostFaceNet",
"Buffalo_L" (InsightFace-based, requires additional dependencies; see Installation)
]
#face verification
result = DeepFace.verify(
img1_path = "img1.jpg",
@ -157,11 +159,13 @@ embedding_objs = DeepFace.represent(
model_name = models[2],
)
```
**Note:** The `Buffalo_L` model uses InsightFace’s `webface_r50.onnx`. If automated download fails, manually download it from [here](https://drive.google.com/file/d/1N0GL-8ehw_bz2eZQWz2b0A5XBdXdxZhg/view?usp=sharing) and place it in `~/.deepface/weights/buffalo_l/`.
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/model-portfolio-20240316.jpg" width="95%" height="95%"></p>
FaceNet, VGG-Face, ArcFace and Dlib are overperforming ones based on experiments - see [`BENCHMARKS`](https://github.com/serengil/deepface/tree/master/benchmarks) for more details. You can find the measured scores of various models in DeepFace and the reported scores from their original studies in the following table.
| Model | Measured Score | Declared Score |
| -------------- | -------------- | ------------------ |
| Facenet512 | 98.4% | 99.6% |
@ -202,6 +206,9 @@ dfs = DeepFace.find(
)
```
**Note:** The `Buffalo_L` model works best with cosine similarity. you can play around with the thresholds as it pleases you.
**Facial Attribute Analysis** - [`Demo`](https://youtu.be/GT2UeN85BdA)
DeepFace also comes with a strong facial attribute analysis module including [`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/), [`facial expression`](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) (including angry, fear, neutral, sad, disgust, happy and surprise) and [`race`](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/) (including asian, white, middle eastern, indian, latino and black) predictions. Result is going to be the size of faces appearing in the source image.