Creates attributes used to adjust face tracking resolution


## Description
no description
## Parameters

<table>
<thead>
	<tr>
		<th>Name</th>
		<th>Type</th>
		<th>Description</th>
	</tr>
</thead>
<tr>
	<td>selfieMode</td>
	<td><div class='bg-emerald-800 px-2 py-px text-white rounded-sm'>boolean</div></td>
	<td>selfieMode</td>
</tr>
<tr>
	<td>maxNumFaces</td>
	<td><div class='bg-orange-800 px-2 py-px text-white rounded-sm'>integer</div></td>
	<td>Maximum number of faces to detect</td>
</tr>
<tr>
	<td>refineLandmarks</td>
	<td><div class='bg-emerald-800 px-2 py-px text-white rounded-sm'>boolean</div></td>
	<td>Whether to further refine the landmark coordinates around the eyes and lips, and output additional landmarks around the irises by applying the Attention Mesh Model</td>
</tr>
<tr>
	<td>minDetectionConfidence</td>
	<td><div class='bg-yellow-800 px-2 py-px text-white rounded-sm'>float</div></td>
	<td>Minimum confidence value ([0.0, 1.0]) from the face detection model for the detection to be considered successful. Default to 0.5.</td>
</tr>
<tr>
	<td>minTrackingConfidence</td>
	<td><div class='bg-yellow-800 px-2 py-px text-white rounded-sm'>float</div></td>
	<td>Minimum confidence value ([0.0, 1.0]) from the landmark-tracking model for the face landmarks to be considered tracked successfully, or otherwise face detection will be invoked automatically on the next input image. Setting it to a higher value can increase robustness of the solution, at the expense of a higher latency. Ignored if static_image_mode is true, where face detection simply runs on every image. Default to 0.5.</td>
</tr>
</table>