Face encoding¶
Face encoding is the process of extracting facial features from a detected face. It outputs a face template to be used for face comparison or face search.
Encoding models¶
Model |
Size |
Description |
|---|---|---|
FaceEncoder9A |
127.36 MB |
This model provides the best accuracy. It is recommended for 1-to-N applications or in an uncontrolled environment. Generated template size is 264 bytes. |
FaceEncoder9B |
1.99 MB |
This model provides a good balance of accuracy, speed, and size. This model is less robust to variations of head pose than the 9A version and should therefore only be used in a controlled environment. Generated template size is 140 bytes. |
FaceEncoder8A |
152.46 MB |
Obsolete. Provided for backward compatibility. Use model 9A whenever possible. Generated template size is 268 bytes. |
FaceEncoder8B |
2.12 MB |
Obsolete. Provided for backward compatibility. Use model 9B whenever possible. Generated template size is 140 bytes. |
Note
Call the FaceLibrary.loadModel Method to load the chosen model on application start.
Hint
On startup, call the FaceEncoder.warmUp Method to prepare inference of the AI model.
Face template¶
A face template is a small representation of a face, only 140 bytes or 264 bytes depending on the encoding model used. It is used for face comparison or face search.
A face template can be also exported to the format required by id3 Face Match-on-Card solution.
Encoding quality¶
The component also provides a quality estimator that aims at predicting the power of matching of a created template.
Important
This functionality requires the model FaceEncodingQualityEstimator_2A to be loaded.
Example¶
The example below demonstrates how to encode a face image and generate a FaceTemplate:
# initializes a FaceEncoder and warm it up.
face_encoder = FaceEncoder(
thread_count=4
)
# warm up the face encoder
face_encoder.warm_up()
# create the template from the detected face.
face_template = face_encoder.create_template(image, face)
# dispose all resources
del face_encoder