مشروع البحث: FACIAL EMOTION IMAGES RECOGNITION BASED ON BINARIZED GENETIC ALGORITHM-RANDOM FOREST
| dc.contributor.advisor | Dr. Yusliza Binti Yusoff | |
| dc.date.accessioned | 2025-09-02T08:48:04Z | |
| dc.date.available | 2025-09-02T08:48:04Z | |
| dc.description | This thesis proposed a facial emotion recognition system based on HOG features and BGA-RF features selection. The study developed a facial emotion recognition system based on HOG features and BGA which has been utilized as a features selection in order to select the most effective features of HOG. Besides, the RF was used as a classifier to classify human facial emotions based on images samples. The input consisted of a set of 11 common human facial emotions which are centre light, glasses, happy, left light, no glasses, normal, right light, sad, sleepy, surprised, and wink with the output being the class to which the utterance was associated with or related to. | |
| dc.description.abstract | Most recognition system of human facial emotions are evaluated in terms of accuracy only, where there are other performance measurements that are considered significant in the evaluation process such as sensitivity, precision, F-measure, and G-mean. Moreover, the most common problem that needs to be solved in face emotion recognition systems is the feature extraction methods, which is similar to the traditional manual feature extraction methods. This traditional method cannot effectively extract features. In this work, | |
| dc.identifier | 711 | |
| dc.identifier.uri | https://dspace.academy.edu.ly/handle/123456789/1741 | |
| dc.subject | GENETIC ALGORITHM-RANDOM FOREST | |
| dc.title | FACIAL EMOTION IMAGES RECOGNITION BASED ON BINARIZED GENETIC ALGORITHM-RANDOM FOREST | |
| dspace.entity.type | Project | |
| project.endDate | 2022 | |
| project.funder.name | علوم الحاسوب | |
| project.investigator | مراد ابراهيم حسين الثابت | |
| project.startDate | 2021 | |
| relation.isOrgUnitOfProject | 784d19bf-029a-47b8-b883-c9daefb34588 | |
| relation.isOrgUnitOfProject.latestForDiscovery | 784d19bf-029a-47b8-b883-c9daefb34588 |