Emoticon Generation, Expression Recognition, and Gender Classification Using Deep Learning in Real-Time

Abstract

Images play an increasingly important role in identifying a person's gender and emotional state in today's digital environment, but there are still methodological hurdles to overcome. Image processing utilizing deep learning algorithms is the way to go. Our study's overarching goal is to find ways to bridge the communication gap through the use of emoticons based on the emotions conveyed in photographs and snapshots. We have utilized the Keras framework to implement a deep learning algorithm called a Convolutional Neural Network (CNN) and evaluated it using Tensor Flow to predict gender. The goal is to create a new dataset of pictures free of noise and then utilize those images as inputs to a convolutional neural network (CNN). The algorithm's result is supposed to be more trustworthy gender identification based on increased accuracy. We have implemented an LSTM-RNN (Long short-term memory recurrent neural network) for emotion identification and facial expression detection. Feature selection is the most crucial step since it will ultimately aid in emoticon generation.