  Emotion being a subjective thing, leveraging knowledge and science behind
labeled data and extracting the components that constitute it, has been a
challenging problem in the industry for many years. With the evolution of deep
learning in computer vision, emotion recognition has become a widely-tackled
research problem. In this work, we propose two independent methods for this
very task. The first method uses autoencoders to construct a unique
representation of each emotion, while the second method is an 8-layer
convolutional neural network (CNN). These methods were trained on the
posed-emotion dataset (JAFFE), and to test their robustness, both the models
were also tested on 100 random images from the Labeled Faces in the Wild (LFW)
dataset, which consists of images that are candid than posed. The results show
that with more fine-tuning and depth, our CNN model can outperform the
state-of-the-art methods for emotion recognition. We also propose some exciting
ideas for expanding the concept of representational autoencoders to improve
their performance.
