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face recognition model comparison

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Sefik Ilkin Serengil 2020-04-21 17:53:13 +03:00 committed by GitHub
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@ -51,9 +51,7 @@ openface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "OpenFace
deepface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "DeepFace") deepface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "DeepFace")
``` ```
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. 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 of each face recognition model is different. Mean ± std. dev. of 7 runs on CPU for each model in my experiments is illustrated in the following table.
| Model | VGG-Face | OpenFace | Google FaceNet | Facebook DeepFace | | Model | VGG-Face | OpenFace | Google FaceNet | Facebook DeepFace |
| --- | --- | --- | --- | --- | | --- | --- | --- | --- | --- |