ML-DCNNet: Multi-level Deep Convolutional Neural Network for Facial Expression Recognition and Intensity Estimation

The human face has a great accumulation and a diversity of facial expressions. It explores the feelings of a person and can be used to judge the emotional intents of the person to a certain level. By using facial detection and recognition systems, varieties of applications are working in computer vision, surveillance system, security, authentication, or verification of a person and home automation system based on digital image processing with the help of the Internet of Things. The state of the art in these applications is to detect expressions with their intensity level. It is an attention-grabbing problem due to the complex nature of facial features, which is associated with emotions. For that purpose, it is essential to develop an innovative deep learning model to detect and estimate the facial expression intensity level. To do this, a multi-level deep convolutional neural network is proposed to recognize facial expression and their intensity level. At the first level, Expression-Net classifies face expressions, and at the second level, Intensity-Net estimates the intensity of the facial expression. Evaluation of the proposed model for facial expression recognition and intensity estimation is carried out by using the extended Cohn–Kanade and Japanese Female Facial Expression datasets. The proposed method shows an outstanding performance in terms of accuracy of 98.8% and 97.7% for both the datasets as compared to state-of-the-art techniques.