Facial Emotion Recognition and Detection in Python Using Deep Learning

Background: There was no technology to speak or convey human expressions. But now, using artificial intelligence deep learning techniques, computers can recognize human facial expression and check movements without manual intervention.

Objective: The facial recognition application plays an important role in many areas such as security, camera surveillance etc. This work presents deep learning algorithm used in facial recognition for accurate identification.

Methodology: Emotional expressivity has always been a simple job for people, but computer programming is much harder to accomplish. In this method, we represent a new method to defect face emotion using neural network coevolutionary. The process is based on CNN network of two parts: the first portion remove the backdrop of the image, the second part removed the face vector. The main focus of this work is to create a Deep Convolutional Neural Network (DCNN) model that classifies 5 different human facial emotions. The model is trained, tested and validated using the manually collected image dataset.

Result and discussion: The results collected from the study are quite interesting. The results of the experiments demonstrate the perfections in face analysis system.  Finally, the performance of automatic face detection and recognition is measured with accuracy.

Future Work: The rapid growth of artificial intelligence has contributed a lot to the technology world. In future we can do this along with mind, that what is going in our mind computer can say.