A pratical implementation of deep neural network for facial emotion recognition
Journal Title: SCIENCE, ENGINEERING AND TECHNOLOGY - Year 2022, Vol 2, Issue 1
Abstract
People's emotions are rarely put into words, far more often they are expressed through other cues. The key to intuiting another's feelings is in the ability to read nonverbal channels, tone of voice, gesture, facial expression and the like. Facial expressions are used by humans to convey various types of meaning in a variety of contexts. The range of meanings extends from basic, probably innate, social-emotional concepts such as "surprise" to complex, culture-specific concepts such as "neglect". The range of contexts in which humans use facial expressions extends from responses to events in the environment to specific linguistic constructs in sign languages. In this paper, we will use an artificial neural network to classify each image into seven facial emotion classes. The model is trained on a database of FER+ images that we assume is large and diverse enough to indicate which model parameters are generally preferable. The overall results show that, the CNN model is efficient to be able to classify the images according to the state of emotions even in real time.
Authors and Affiliations
Ferroudja Djellali, Emir Deljanin
Keywords
Related Articles
- EP ID EP709793
- DOI https://doi.org/10.54327/set2022/v2.i1.26
- Views 77
- Downloads 0
Analytical and Numerical Contact Stress Analysis of Spur Gears
The damage that occurs on the teeth flank of cylindrical gears is a complex phenomenon and depends on many factors. The most common cause for these damages is the high contact stress of the meshing gears. Although this c...
HetroTraffSim: A Novel Traffic Simulation Software for Heterogeneous Traffic Flow
Traffic simulation software (TSS) is employed for planning, designing, and managing road networks. Among existing TSS, only SUMO and HETROSIM can be used for heterogeneous traffic. The objective of this work is to develo...
Prediction of user throughput in the mobile network along the motorway and trunk road
The main goal of this research is to create a machine learning model for predicting user throughput in the mobile 4G network of the network provider M:tel Banja Luka, Bosnia and Herzegovina. The geographical area of the...
A practical implementation of machine learning in predicting breast cancer
Cancer is the leading disease in the world by the increasing number of new patients and deaths every year. Hence, it is the most feared disease of our time. It is believed that lung cancer and breast cancer are most comm...
Rough estimation of cell numbers in 5G networks using simple mathematical calculations
This paper focuses on the problem of 5G network cell planning. In addition, it presents an example of a rough estimation of the required number of cells or base stations in a certain area for arbitrary number of users wh...