This type of Annotation is mainly used to plot characteristics in the data, such as facial recognition to detect facial features expressions and emotions and happiness. It is also used to annotate body position and alignment using pose point annotations. It mainly carries out the task of features of interest. This type of annotation is useful for detecting small objects and shape variations by creating dots across the image.

The process of annotating landmarks can require a significant amount of time and tedious labor, which motivates the need for algorithms that can automatically annotate landmarks.

Following are the use cases regarding landmarking annotation:

Face Recognition

  • Computer vision projects also use a landmark to pinpoint features of a face with accurate facial recognition. The annotator adds numerous points on the face of a person with unique features.
  • This helps in differentiating one face from another.
  • Infact now a days mobile phone manufacturers have added this special tool for face unlocking features of smartphones which is essential for data security.

Sports Industry

  • The sports industry becomes the ultimate edge for the coaches to analyze performances of any player and suggest the required changes.
  • The players also analyze their individual performance to sort out their own mistakes, which is essential for every player’s own development as well.
  • It also analyzes the strategies of their opponents and formulates them, by always gathering the ideas and strategies in order to devise a strategy to beat their opponents in their own game.
  • Each & every action of a player is recorded and monitored to make it usable as training data for ML models in the sports industry.

Animal Behavior

  • Land marking annotation is often a crucial step in the study of animal behavior and kinematics. Kinematics is generally used to describe the motion of the body and limbs of an animal.
  • In recent years we have witnessed that landmarking annotation plays an active role in detecting an animal’s characteristics as well.
  • Humans communicate emotions and physical sensations through a diverse range of facial expressions. Animals possess far fewer facial muscles and display their feelings in subtle ways that are hard for humans to interpret.
  • Many animals also hide feelings of pain or discomfort due to the evolutionary need to project strength and health to potential predators. Failure to accurately identify the true emotional state of animals can lead to mistakes in treatment and healthcare, and can affect animal welfare overall.

Human Poses

Basically, this is a common vision task that is usually tackled through Deep Learning. This is a way of identifying and classifying the joints in the human body.

  • It is a way to capture a set of positions for each joint which is arms, head, torso, which we call key point that can describe a pose of a person.
  • Computer vision is generally used to understand geometric and motion information of the human body which can be very intricate.
  • The poses could relate to doing exercises, running, playing or any kind of action done by the human body. This is where landmark annotation plays a crucial part in identifying these actions.


The above use cases are something that all of us are aware of, and with landmark annotation we can absolutely identify things that would be beneficial for the world itself, because we would be creating datasets for Machine Learning so that these algorithms which we provide would be accurate.