Find faces in an image:

Find faces in frames of a video:

Highlight detected faces on the video:

Compute the face bounding box:

Extract the portion of the image corresponding to the bounding box:

Extract multiple properties:

Extract features available in FacialFeatures:

Use a computable property as a test argument:

Some test results can be used as shortcuts for the whole test:

Find faces of all sizes:

Specify a maximum face size in pixels:

Specify a minimum face size in pixels:

Use Scaled to specify the face size relative to the image dimensions:

By default, all the detected faces are returned:

Use AcceptanceThresholdt to return only detections with strength greater than t:

Use AcceptanceThreshold0 to return all the detected faces:

By default, all the detected faces are returned:

Use MaxFeaturesn to return only the n strongest detections:

By default, all the detected faces are returned, regardless of their overlapping:

Find only non-overlapping faces:

Allow up to 2 percent of overlap:

FindFaces automatically picks the detection method:

Specify the method to use:

Use the combined result from multiple methods:

The "LocalBinaryPatterns" method is a cascade classifier that works on a histogram of the gradients (HoG) features. It is a very fast and lightweight multiscale method:

The "Haar" method is a cascade classifier working on Haar features. It is more robust than "LocalBinaryPatterns" but generally slower:

The "SingleShotDetector" method is based on a single neural net evaluation, and it is very stable with respect to variations in pose, illumination, occlusion and subjects. It is slower compared to two other methods:

The bounding box of a detection can be padded beyond the image boundary:

Specify a custom padding specification:

FindFaces by default returns a fairly tight crop around each face:

Specify a padding size to be added around each face:

Specify a negative padding to crop more:

Specify a scaled padding:

Compare detected faces using different paddings:

Use PerformanceGoal"Quality" to emphasize the quality of the result:

Use PerformanceGoal"Speed" to emphasize the speed of computation:

By default, detections are sorted by "Confidence":

Specify a different property: