Hand Gesture Recognition using Adaptive Network Based Fuzzy Inference System and K-Nearest Neighbor
Published at : 29 Apr 2017
Volume : IJtech
Vol 8, No 3 (2017)
DOI : https://doi.org/10.14716/ijtech.v8i3.3146
Mufarroha, F.A., Utaminingrum, F., 2017. Hand Gesture Recognition using Adaptive Network Based Fuzzy Inference System and K-Nearest Neighbor. International Journal of Technology, Volume 8(3), pp. 559-567
| Fifin Ayu Mufarroha | Computer Vision Research Group, Departement of Computer Science, Faculty of Computer Science, Universitas Brawijaya |
| Fitri Utaminingrum | Computer Vision Research Group, Departement of Computer Science, Faculty of Computer Science, Universitas Brawijaya |
The purpose of the study was to investigate hand gesture recognition. The hand gestures of American Sign Language are divided into three categories—namely, fingers gripped, fingers facing upward, and fingers facing sideways—using the adaptive network-based fuzzy inference system. The goal of the classification was to speed up the recognition process, since the process of recognizing the hand gesture takes a longer time. All pictures in all of the categories were recognized using K-nearest neighbor. The procedure involved taking real-time pictures without any gloves or censors. The findings of the study show that the best accuracy was obtained when the epochs score was 10. The proposed approach will result in more effective recognition in a short amount of time.
Adaptive Network Based Fuzzy Inference System (ANFIS); American Sign Language (ASL); Hand gesture; K-nearest Neighbor (K-NN)