Face Detection
Description
Face detection is a tool that allows the user to detect faces in images using varieties of methods, it is useful for security applications.
Settings
Detection Model
The detection model is the type of model that will be used to detect the faces in the images.
Supported Methods
Here are the supported methods:
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- A lightweight face detection model that is designed for edge devices.
- Slow inference speed, but high accuracy.
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- A face detection model that is based on the TensorFlow framework.
- Slow inference speed, but high accuracy.
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- A face detection model that is based on the Local Binary Pattern (LBP) algorithm.
- Fast inference speed, but low accuracy.
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- Fast inference speed, but low accuracy.
NMS Threshold
YuNET OpenCV - TensorFlowThe NMS threshold is the minimum threshold for non-maximum suppression. Non-maximum suppression is a technique used to reduce the number of bounding boxes by removing the ones that overlap too much with other bounding boxes. A lower NMS threshold will result in fewer bounding boxes, but with potentially higher accuracy, whereas a higher threshold will result in more bounding boxes, but with lower accuracy.
Confidence Threshold
YuNET OpenCV - TensorFlowThe confidence threshold is the minimum confidence score a face detection should have to be considered valid. Face detections with confidence scores below this threshold will be discarded. A higher confidence threshold will result in fewer face detections, but with higher accuracy, whereas a lower threshold will result in more face detections, but with potentially lower accuracy.
Top K Value
YuNETThe top K value is the maximum number of face detections that will be returned, before NMS. A higher top K value will result in more face detections, but with potentially lower accuracy, whereas a lower top K value will result in fewer face detections, but with higher accuracy.
Display Results
Overlay Results
Whether to draw the results on top of the image frame.