github/deepface
1
1
Fork 0
mirror of https://github.com/serengil/deepface.git synced 2025-06-06 19:45:21 +00:00
Code Issues Packages Projects Releases Wiki Activity

timeit for face recognition

Browse Source
This commit is contained in:
Sefik Ilkin Serengil 2020-04-21 12:18:21 +03:00 committed by GitHub
parent 26444c0c8f
commit b7ba13f273
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 7 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
README.md
View File

@ -53,6 +53,13 @@ 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. 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 of each face recognition model is different. Mean ± std. dev. of 7 runs for each model in my experiments is illustrated in the following table.
| Model | VGG-Face | OpenFace | Google FaceNet | Facebook DeepFace |
| --- | --- | --- | --- | --- |
| Building | 2.35 s ± 46.9 ms | 6.37 s ± 1.28 s | 25.7 s ± 7.93 s | 23.9 s ± 2.52 s |
| Verification | 897 ms ± 38.3 ms | 616 ms ± 12.1 ms | 684 ms ± 7.69 ms | 605 ms ± 13.2 ms |
## Similarity ## Similarity
These models actually find the vector embeddings of faces. Decision of verification is based on the distance between vectors. Distance could be found by different metrics such as [`Cosine Similarity`](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance and L2 form. The default configuration finds the **cosine similarity**. You can alternatively set the similarity metric while verification as demostratred below. These models actually find the vector embeddings of faces. Decision of verification is based on the distance between vectors. Distance could be found by different metrics such as [`Cosine Similarity`](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance and L2 form. The default configuration finds the **cosine similarity**. You can alternatively set the similarity metric while verification as demostratred below.