Due to the emergence of social media and high mobile usage, People nowadays share their feelings, thoughts, and emotions on social media like Facebook and Twitter. People express their feelings or emotions on social media by posting pictures, videos, and text messages. People can quickly identify how others feel inside by looking at their faces. Based on the facial expressions revealed by a person, anyone can understand their emotional state. The emotional states can be happy, sad, angry, surprised, etc. By analysing others’ emotional states, psychologists or therapists can give proper guidance so they can lead a peaceful life. People want to understand tiny facial movements called Micro-Expressions (ME) because they reveal a person’s true feelings. Researchers widely use micro-expressions in areas like lie detection, clinical evaluation, human-computer interaction, and depression analysis. Experts or well-trained persons in the psychological field mainly study Micro-Expressions to identify them. However, since micro-expressions move quickly, it can be difficult for psychologists or therapists to assess them properly, highlighting the necessity for computer vision-based methods. Due to continuous advances in computer technology, computer vision-based ME analysis tools have gradually replaced psychological-based approaches in recent years. However, identifying microexpressions is difficult because they are short and last less than a second. This paper provides the researchers insight into different Micro-Expression Recognition (MER) systems.