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Sefik Ilkin Serengil 2022-07-15 11:01:27 +01:00 committed by GitHub
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@ -111,6 +111,8 @@ assert len(embedding) == 2622
Here, embedding is also plotted with 2622 slots horizontally. Each slot is corresponding to a dimension value in the embedding vector and dimension value is explained in the colorbar on the right. Here, embedding is also plotted with 2622 slots horizontally. Each slot is corresponding to a dimension value in the embedding vector and dimension value is explained in the colorbar on the right.
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/embedding.jpg" width="95%" height="95%"></p>
**Similarity** **Similarity**
Face recognition models are regular [convolutional neural networks](https://sefiks.com/2018/03/23/convolutional-autoencoder-clustering-images-with-neural-networks/) and they are responsible to represent faces as vectors. We expect that a face pair of same person should be [more similar](https://sefiks.com/2020/05/22/fine-tuning-the-threshold-in-face-recognition/) than a face pair of different persons. Face recognition models are regular [convolutional neural networks](https://sefiks.com/2018/03/23/convolutional-autoencoder-clustering-images-with-neural-networks/) and they are responsible to represent faces as vectors. We expect that a face pair of same person should be [more similar](https://sefiks.com/2020/05/22/fine-tuning-the-threshold-in-face-recognition/) than a face pair of different persons.
@ -180,6 +182,9 @@ obj = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", detector_b
#face recognition #face recognition
df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backends[4]) df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backends[4])
#embeddings
embedding = DeepFace.represent(img_path = "img.jpg", detector_backend = backends[4])
#facial analysis #facial analysis
demography = DeepFace.analyze(img_path = "img4.jpg", detector_backend = backends[4]) demography = DeepFace.analyze(img_path = "img4.jpg", detector_backend = backends[4])