Dry Eye Disease (DED) is one of the most common ocular diseases: over fivepercent of US adults suffer from DED. Tear film instability is a known factorfor DED, and is thought to be regulated in large part by the thin lipid layer thatcovers and stabilizes the tear film. In order to aid eye related disease diagnosis,this work proposes a novel paradigm in using computer vision techniques tonumerically analyze the tear film lipid layer (TFLL) spread. Eleven videos ofthe tear film lipid layer spread are collected with a micro-interferometer and asubset are annotated. A tracking algorithm relying on various pillar computervision techniques is developed. Our method can be found at https://easytear-dev.github.io/.