An Intelligent Facial Expression Recognition System with Emotion Intensity Classification

Recognition of facial emotion based on SOAR model

Introduction

Expressing emotions play a special role in daily communication, and one of the most essential methods in detecting emotions is to detect facial emotional states. Therefore, one of the crucial aspects of the natural human-machine interaction is the recognition of facial expressions and the creation of feedback, according to the perceived emotion.

Methods

To implement each part of this model, two main steps have been introduced. The first step is reading the video and converting it to images and preprocessing on them. The next step is to use the combination of 3D convolutional neural network (3DCNN) and learning automata (LA) to classify and detect the rate of facial emotional recognition. The reason for choosing 3DCNN in our model is that no dimension is removed from the images, and considering the temporal information in dynamic images leads to more efficient and better classification. In addition, the training of the 3DCNN network in calculating the backpropagation error is adjusted by LA so that both the efficiency of the proposed model is increased, and the working memory part of the SOAR model can be implemented.

Results and discussion

Due to the importance of the topic, this article presents an efficient method for recognizing emotional states from facial images based on a mixed deep learning and cognitive model called SOAR. Among the objectives of the proposed model, it is possible to mention providing a model for learning the time order of frames in the movie and providing a model for better display of visual features, increasing the recognition rate. The accuracy of recognition rate of facial emotional states in the proposed model is 85.3%. To compare the effectiveness of the proposed model with other models, this model has been compared with competing models. By examining the results, we found that the proposed model has a better performance than other models.

Lighting environmental assessment in enclosed spaces based on emotional model

Lighting assessment in special operating environments like enclosed spaces is of great research significance and value. In addition to investigating the visual ergonomics of workers, the emotional monitoring and guidance of workers in enclosed spaces is also a research focus. Based on the Circumplex emotion model theory, this paper designs an experiment to assess emotions in an enclosed space with 6 different lighting settings (2 correlated color temperature (CCT) × 3 illuminance levels). From the perspective of subjective assessment, participants used a rapid sensory analysis method (Check-all-that-apply, CATA) and a Subjective Coordinate Scale (SCS) method for rapid ambience perception checking and emotional self-reporting of lighting settings. From the perspective of objective evaluation, the participants' facial expressions were recorded during the experiment using a camera, and then the recorded facial expressions were automatically analyzed and predicted using FaceReader (FRE) software. The CATA and SCS showed similar results, with the 3100 K × 600 lx, 3100 K × 1000 lx and 6500 K × 600 lx lighting settings creating a relaxed, pleasant emotion in participants, the 6500 K × 1000 lx setting creating an excited, tense atmosphere, and the low illumination level settings of 3100 K × 250 lx and 6500 K × 250 lx made participants feel tired and frustrated. The results of the objective emotion analysis indicate that the FRE was able to effectively identify differences in participants' emotions in response to different lighting settings and was consistent with the results of participants' subjective emotion reports. This laboratory study validates that the three methods can effectively assess the enclosed space lighting settings, and provides a reference for further research on the enclosed space lighting settings and emotional monitoring of workers in the future.

Facial Recognition System Based on Genetic Algorithm Improved ROI-KNN Convolutional Neural Network

The facial recognition system is an application tool that uses artificial intelligence technology and biometrics technology to analyze and recognize the facial feature information of the human face. It is widely used in various fields, such as attendance and access control management in schools and companies, identity monitoring in stations and stores, facial recognition for fugitive criminals, and facial payment on mobile terminals. However, due to the short development time of the facial recognition system, the facial recognition system has the problem of low recognition accuracy when the recognized object is not cooperative. Although some scholars have proposed the region of interest (ROI)-K nearest neighbor algorithm (KNN) convolutional neural network theory by using the ROI and KNN and applied it to face recognition, the facial recognition system based on ROI-KNN convolutional neural network did not solve the problems of insufficient facial recognition accuracy and insufficient security. Under the conditions of insufficient illumination, excessive expression change, occlusion, high similarity of different individuals, and dynamic recognition, the recognition effect of the facial recognition system based on the ROI-KNN convolutional neural network is relatively limited. Therefore, to make the recognition accuracy of the facial recognition system higher and to make the facial recognition system play a greater role in the social and economic fields, this paper used the adaptive quantum genetic algorithm, the improved marker line graph genetic algorithm, and the feature weight value genetic algorithm to study the facial recognition system of the ROI-KNN convolutional neural network. The research results showed that after improving the ROI-KNN convolutional neural network based on the genetic algorithm, the recognition accuracy of the facial recognition system was increased by 4.99%, the recognition speed was increased by 7.46%, and the recognition security was increased by 2.66%.