An Investigation into Modern Facial Expressions Recognitionby a Computer
2019
Facial Expressions Recognition using the Convolution
Neural Network has been actively researched upon in the last decade
due to its high number of applications in the human-computer
interaction domain. As Convolution Neural Networks have the
exceptional ability to learn, they outperform the methods using
handcrafted features. Though the state-of-the-art models achieve
high accuracy on the lab-controlled images, they still struggle for
the wild expressions. Wild expressions are captured in a real-world
setting and have natural expressions. Wild databases have many
challenges such as occlusion, variations in lighting conditions and
head poses. In this work, I address these challenges and propose a
new model containing a Hybrid Convolutional Neural Network with a
Fusion Layer. The Fusion Layer utilizes a combination of the
knowledge obtained from two different domains for enhanced feature
extraction from the in-the-wild images. I tested my network on two
publicly available in-the-wild datasets namely RAF-DB and
AffectNet. Next, I tested my trained model on CK+ dataset for the
cross-database evaluation study. I prove that my model achieves
comparable results with state-of-the-art methods. I argue that it
can perform well on such datasets because it learns the features
from two different domains rather than a single domain. Last, I
present a real-time facial expression recognition system as a part
of this work where the images are captured in real-time using
laptop camera and passed to the model for obtaining a facial
expression label for it. It indicates that the proposed model has
low processing time and can produce output almost
instantly.
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