  Faces and their expressions are one of the potent subjects for digital
images. Detecting emotions from images is an ancient task in the field of
computer vision; however, performing its reverse -- synthesizing facial
expressions from images -- is quite new. Such operations of regenerating images
with different facial expressions, or altering an existing expression in an
image require the Generative Adversarial Network (GAN). In this paper, we aim
to change the facial expression in an image using GAN, where the input image
with an initial expression (i.e., happy) is altered to a different expression
(i.e., disgusted) for the same person. We used StarGAN techniques on a modified
version of the MUG dataset to accomplish this objective. Moreover, we extended
our work further by remodeling facial expressions in an image indicated by the
emotion from a given text. As a result, we applied a Long Short-Term Memory
(LSTM) method to extract emotion from the text and forwarded it to our
expression-altering module. As a demonstration of our working pipeline, we also
create an application prototype of a blog that regenerates the profile picture
with different expressions based on the user's textual emotion.
