Scientists are starting to be able to accurately read animal facial expressions and understand what they communicate. Facial expressions project our internal emotions to the outside world. Without your best friend saying a word, you know — by seeing the little wrinkles around her eyes, her rounded, raised cheeks and upturned lip corners — that she got that promotion she wanted. What if we could just as easily read the faces of other living beings? Researchers are developing coding systems that enable them to objectively read animal facial expressions rather than inferring or guessing at their meaning.
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100 Words for Facial Expressions
Facial Action Coding System - Wikipedia
Body language refers to the nonverbal signals that we use to communicate. According to experts, these nonverbal signals make up a huge part of daily communication. It has been suggested that body language may account for between 60 percent to 65 percent of all communication. In many cases, you should look at signals as a group rather than focusing on a single action. Think for a moment about how much a person is able to convey with just a facial expression. A smile can indicate approval or happiness.
Facial Expression Recognition with Keras
We propose an algorithm for facial expression recognition which can classify the given image into one of the seven basic facial expression categories happiness, sadness, fear, surprise, anger, disgust and neutral. PCA is used for dimensionality reduction in input data while retaining those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components and ignoring higher-order ones. Such low-order components contain the "most important" aspects of the data. The extracted feature vectors in the reduced space are used to train the supervised Neural Network classifier. This approach results extremely powerful because it does not require the detection of any reference point or node grid.
Friesen, and published in Hager published a significant update to FACS in Due to subjectivity and time consumption issues, FACS has been established as a computed automated system that detects faces in videos, extracts the geometrical features of the faces, and then produces temporal profiles of each facial movement. Using FACS  human coders can manually code nearly any anatomically possible facial expression, deconstructing it into the specific action units AU and their temporal segments that produced the expression.