Model Capability

  • Due to the capability of Face Extraction based on the Deep Learning models, it is highly dependent on the characteristics of the training data and the characteristics of the real case conditions when the model is applied. There are cases where Nodeflux Face Recognition model fails to generalize similar facial features. Similar to the human eye capability, on several occasions it is failed to distinguish two people who has similar characteristics at certain angles and lighting.

  • The face biometrics model has limited capability to distinguish identical twin cases.

  • The angle and lighting condition on which the camera located may affect the result of face matching.

  • The face pose must be fully capture by the CCTV camera.

  • Sunglasses and other attributed that covering the face area may reduce the accuracy of the face Biometrics.

Result Interpretation

The accuracy of the Face Recognition model highly dependents on the captured. In small cases it may present False Positive result due to several conditions such as face attributes, angle and lighting.

Our NFV4-FR is designed to accurately identify human faces in images and videos. However, there may be rare cases where the model produces a false positive result if the image contains features that resemble a human face, such as an animal or an animated character. We strive to maintain a low error budget, but please be aware that there is a small possibility of false positive results occurring. If you have any concerns about the accuracy of the model, please don't hesitate to contact us for further assistance.

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