Vol 8, No 3 (2017) > Electrical, Electronics and Computer Engineering >

Hand Gesture Recognition using Adaptive Network Based Fuzzy Inference System and K-Nearest Neighbor

Fifin Ayu Mufarroha, Fitri Utaminingrum


Abstract: 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.
Keywords: Adaptive Network Based Fuzzy Inference System (ANFIS); American Sign Language (ASL); Hand gesture; K-nearest Neighbor (K-NN)

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