A Unified Framework of Deep Learning-Based Facial Expression Recognition System for Diversified Applications

Abstract

This work proposes a facial expression recognition system for a diversified field of applications. The purpose of the proposed system is to predict the type of expressions in a human face region. The implementation of the proposed method is fragmented into three components. In the first component, from the given input image, a tree-structured part model has been applied that predicts some landmark points on the input image to detect facial regions. The detected face region was normalized to its fixed size and then down-sampled to its varying sizes such that the advantages, due to the effect of multi-resolution images, can be introduced. Then, some convolutional neural network (CNN) architectures were proposed in the second component to analyze the texture patterns in the facial regions. To enhance the proposed CNN model’s performance, some advanced techniques, such data augmentation, progressive image resizing, transfer-learning, and fine-tuning of the parameters, were employed in the third component to extract more distinctive and discriminant features for the proposed facial expression recognition system. The performance of the proposed system, due to different CNN models, is fused to achieve better performance than the existing state-of-the-art methods and for this reason, extensive experimentation has been carried out using the Karolinska-directed emotional faces (KDEF), GENKI-4k, Cohn-Kanade (CK+), and Static Facial Expressions in the Wild (SFEW) benchmark databases. The performance has been compared with some existing methods concerning these databases, which shows that the proposed facial expression recognition system outperforms other competing methods.

Smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework

“…Since the facial expression recognition system suffers from the over-fitting problem, the lack of sufficient training samples and bias cause expression variations such as age variation, head-pose, identity bias, and illumination variation. The proposed methods focus on these issues and overcome the computational complexity of the CNN model [ 38 ]. The CNN architectures’ hybrid nature helps extract both the shape and texture information from the images.…”

FaceTopoNet: Facial Expression Recognition Using Face Topology Learning

“…In this regard, they propose different convolutional neural network architectures to handle multiresolution facial images. Hossain et al in [46] propose a three-component FER system. First component includes the detection of facial regions, the second component uses CNNs to extract the embeddings, and the third component adopts different techniques like transfer learning to extract more distinctive embeddings.…”

Human-Computer Interaction Using Deep Fusion Model-Based Facial Expression Recognition System

“…The deep learning-based approaches work in an encapsulated way by the combined effect of both feature learning and classification task. There are several deep learningbased approaches, and among them, the convolutional neural network (CNN) [29] based models have been employed in this work. The CNN based approaches are based on the core building blocks of convolutional layers, pooling layers, fully connected layers, and dense layers [29].…”

“…There are several deep learningbased approaches, and among them, the convolutional neural network (CNN) [29] based models have been employed in this work. The CNN based approaches are based on the core building blocks of convolutional layers, pooling layers, fully connected layers, and dense layers [29]. The convolutional layer is the layer where the input is an image that has been convoluted with several distinct filters (kernels).…”