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face_recognition
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512d2532
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face_recognition
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512d2532
编写于
9月 26, 2020
作者:
A
Adam Geitgey
提交者:
GitHub
9月 26, 2020
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Merge pull request #1213 from timgates42/bugfix_typo_euclidean
docs: Fix simple typo, eucledian -> euclidean
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3337c1e0
8057a2cf
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examples/face_recognition_knn.py
examples/face_recognition_knn.py
+1
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examples/facerec_ipcamera_knn.py
examples/facerec_ipcamera_knn.py
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examples/face_recognition_knn.py
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512d2532
...
@@ -7,7 +7,7 @@ and make a prediction for an unknown person in a feasible computation time.
...
@@ -7,7 +7,7 @@ and make a prediction for an unknown person in a feasible computation time.
Algorithm Description:
Algorithm Description:
The knn classifier is first trained on a set of labeled (known) faces and can then predict the person
The knn classifier is first trained on a set of labeled (known) faces and can then predict the person
in an unknown image by finding the k most similar faces (images with closet face-features under eucl
edi
an distance)
in an unknown image by finding the k most similar faces (images with closet face-features under eucl
ide
an distance)
in its training set, and performing a majority vote (possibly weighted) on their label.
in its training set, and performing a majority vote (possibly weighted) on their label.
For example, if k=3, and the three closest face images to the given image in the training set are one image of Biden
For example, if k=3, and the three closest face images to the given image in the training set are one image of Biden
...
...
examples/facerec_ipcamera_knn.py
浏览文件 @
512d2532
...
@@ -7,7 +7,7 @@ and make a prediction for an unknown person in a feasible computation time.
...
@@ -7,7 +7,7 @@ and make a prediction for an unknown person in a feasible computation time.
Algorithm Description:
Algorithm Description:
The knn classifier is first trained on a set of labeled (known) faces and can then predict the person
The knn classifier is first trained on a set of labeled (known) faces and can then predict the person
in a live stream by finding the k most similar faces (images with closet face-features under eucl
edi
an distance)
in a live stream by finding the k most similar faces (images with closet face-features under eucl
ide
an distance)
in its training set, and performing a majority vote (possibly weighted) on their label.
in its training set, and performing a majority vote (possibly weighted) on their label.
For example, if k=3, and the three closest face images to the given image in the training set are one image of Biden
For example, if k=3, and the three closest face images to the given image in the training set are one image of Biden
...
...
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