Paper Info
Title
Hand Component Decomposition For The Hand Gesture Recognition Based On Fingerpaint Dataset
Abstract
In the human-machine interaction system, hand component decomposing is important to recogtlize the human gesture. This paper proposes a method to decompose the hand component for the hand gesture recognition from human body image of FingerPaint dataset which is Microsoft research open data. We choose 36,750 randomly images for training and choose the remaining 15,750 images for the testing from the FingerPaint dataset. We conducted the PFACA(Proportion of frames with average classification accuracy) for the accuracy of the hand component(thumb, index finger, middle finger, ring finger, pinky, palm, wrist). In the results of five times repeated experiments that we showed maximum of 0.9849042 and minimum of 0.949042 at a frame of more than 0.2 of Epsilon.
Year
DOI
Venue
2019
10.1109/ICUFN.2019.8806032
2019 ELEVENTH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS (ICUFN 2019)
Keywords
Field
DocType
Hand Component, Hand Gesture, Hand Component Decomposition, FingerPaint, Human Machine Interaction
Computer vision,Index finger,Thumb,Ring finger,Middle finger,Gesture,Computer science,Gesture recognition,Artificial intelligence,Distributed computing,Human machine interaction
Conference
ISSN
Citations 
PageRank 
2165-8528
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
In Seop Na100.34
Soo-Hyung Kim219149.03
Chil-Woo Lee301.35
Hai Duong Nguyen452.82