  Facial Expression Recognition is a vital research topic in most fields
ranging from artificial intelligence and gaming to Human-Computer Interaction
(HCI) and Psychology. This paper proposes a hybrid model for Facial Expression
recognition, which comprises a Deep Convolutional Neural Network (DCNN) and
Haar Cascade deep learning architectures. The objective is to classify
real-time and digital facial images into one of the seven facial emotion
categories considered. The DCNN employed in this research has more
convolutional layers, ReLU Activation functions, and multiple kernels to
enhance filtering depth and facial feature extraction. In addition, a haar
cascade model was also mutually used to detect facial features in real-time
images and video frames. Grayscale images from the Kaggle repository (FER-2013)
and then exploited Graphics Processing Unit (GPU) computation to expedite the
training and validation process. Pre-processing and data augmentation
techniques are applied to improve training efficiency and classification
performance. The experimental results show a significantly improved
classification performance compared to state-of-the-art (SoTA) experiments and
research. Also, compared to other conventional models, this paper validates
that the proposed architecture is superior in classification performance with
an improvement of up to 6%, totaling up to 70% accuracy, and with less
execution time of 2098.8s.
