Human emotion detection is implemented in many areas
requiring additional security or information about the person. Human emotions
can be classified as: fear, contempt, disgust, anger, surprise, sad, happy, and
neutral.
Facial Emotion Recognition is a technology used for analysing sentiments
by different sources, such as pictures and videos.
FER typically has four steps. The first is to detect a face
in an image and draw a rectangle around it and the next step is to detect
landmarks in this face region. The third step is extracting spatial and
temporal features from the facial components. The final step is to use a
Feature Extraction (FE) classifier and produce the recognition results using
the extracted features.
The FER 2013 dataset is usually used and also several images for the five emotions are selected.(The
emotions considered were happy, sad, angry, fear and neutral). These images are
converted into NumPy arrays and landmark features are identified and extracted.
A CNN model was developed with four phases where the first three phases had
convolution, pooling, batch normalization and dropout layers. The final phase
consists of flatten, dense and output layers
Convolutional neural network (CNN) is an algorithm of deep
learning. Lecun first proposed its idea in 1989, and in 1998 proposed the
application of this algorithm to handwritten digit recognition. In 2012, Alex Krizhevsky won the Imagenet 2012
competition with CNN.
CNN can input image directly and get the final
classification result without data preprocessing. By building a neural network
model with a certain depth and combining nonlinear operations such as
convolution and pooling, we can realize two important functions of imitating
the hierarchical processing of human brain and local perception of visual
nerve. It has been proved that the network has achieved good results in face
recognition, speech recognition, vehicle detection and target tracking. One role of a CNN is to reduce images into a
form which is easier to process without losing features that are critical for
good prediction.
According to some psychologists, communication occurring
through facial expressions account for about 55% of communication, so machines
can offer us more help if they are able to perceive and recognize human
emotions.
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