# Robust Facial Expression Recognition Using an Evolutionary Algorithm with a Deep Learning Model
## 1 Introduction  
Facial expressions have a significant role in presenting the emotions of humans, which might affect day-to-day life by changing our memory, attention, and perceptions. Facial expressions might precisely express the true emotions of others. Humans can learn the inner thoughts of others via facial expressions [1]. Psychologists reported that facial expression is prominent in the day-to-day interactions of humans, making up 55%, of communication, far greater than the written language (7%), speech (38%), etc. [2]. On the other hand, facial expressions are unaffected by age, race, gender, or cultural background and follow from facial muscle movements [3]. Consequently, facial expressions are an effective means of identifying emotions. The study of facial expression detection is vital for progressing artificial intelligence and other fields, and it is based on computer technology that could allow intelligent devices such as robots to identify and better understand our emotions,accomplish barrier-free communication between machines and humans, actively judge human emotion, and better serve humans [4]. Currently, automated FER is the most important task in the field of computer science [5]. An expression could be transported via communication and gestures. It does not depend on the facial expressions of humans. The authors emphasize that a person could orally transmit around 7% of data context, whereas 38% are transported via rhythm, voice tone, and how slowly or speedily a person talks [6]. The applications of facial expressions cover a wide swatch of functions in our society and are not constrained to this field. In medical science, FER is useful for bipolar patients. Physicians try to monitor and detect the behaviors of a patient, like how they behave during their disease and how a bipolar patient feels [7]. An intelligent FER technique such that face images are provided as input and could identify the expression of humans. There exist, overall, eight expressions, including happy, fearful, sad, surprised, angry, neutral, disgusted, and contempt-filled [8]. FER uses a deep learning (DL)-based techniques to help it extract facial expressions and features, which greatly improves its performance. However, FER is prone to complex problems, such as slow recognition speed, trouble extracting facial features, and low recognition accuracy. The key concept of the DL technique is to construct an artificial neural network (ANN) by continuous training of enormous quantities of information to satisfy certain requirements [9]. The goal of a DL algorithm is to retrieve the information confined in the input hierarchically through the construction of multi-layer neural networks (MNNs); this relates to the outlining of hidden layers among the input and output layers of a single computing layer perceptron as an internal description of \"input mode\", such that it becomes a multilayer perceptron (MLP), and the neurons between neighboring layers are interconnected with one another. [10]. Applied science in digital image processing and visualization is now one of the fastest-growing areas of information technology. It has various applications in medical imaging, remote sensing, industrial inspection, computer vision and robotics, image editing, and information visualization. With the rapid growth of multimedia content in social media and smartphone applications, innovative image processing tools and programs for creating featured photographs to improve the aesthetics, entertainment, publicity, and security of these applications are gaining popularity. We developed the Robust Facial Expression Recognition using an Evolutionary Algorithm with Deep Learning (RFER-EADL) model. As a preprocessing step, the RFER-EADL approach employs histogram equalization (HE). In addition, for feature extraction, the chimp optimization algorithm (COA) with a densely linked network (DenseNet-169) model is applied.