Face Emotional Detection using Computational Intelligence based Ubiquitous Computing

The ubiquitous use of industrial technology 4.0 is increasing mainly to facilitate human work. This is because ubiquitous is a small and inexpensive device but has limitations in resources. Research on ubiquitous continues to be developed especially in the use of sensors in ubiquitous. Optimization of face sensors for face detection has also been done. High computing for face detection optimization causes overload on ubiquitous and low accuracy of face detection. This is because resources are limited to ubiquitous. This study developed the human face emotional detection using Computational Intelligence in ubiquitous devices. The computations carried out on ubiquitous devices to detect facial emotions using the K-Means algorithm to segment colors in the results of face images. Photos of segmentation were carried out by histogram to get the gray value. Then, the value is matched with the dataset in the database. The dataset has been classified based on 3 face emotional conditions which are happy, normal, and sad. The dataset classification uses the Nearest Neighbors algorithm. Results of the system testing are done by dataset testing and direct testing on the application. The test results were lined up with an average accuracy of 77.5% with an error of 22.5%.