How is something like this addressed in ML? In traditional software development there are tools like automated tests & static analysis to prevent your customers running into these types of embarrassing issues.

Author here, this is a known issue and being worked on for handtrack.js 2.0.

The face/head errors is likely an artifact of transfer learning (handtrackjs is finetuned on the object detection model trained using the coco object detection dataset which contains a person category).

Current approach being explored to expand the training data to include faces/heads such that the model better discriminates this from hands.

There are also size and speed optimizations being explored (smaller models such as efficientdet, quantization).