Open AccessHand detection and segmentation using smart path tracking fingers as features and expert system classifier
Author(s) -
Khaled N. Yasen,
Fahad Layth Malallah,
Lway Faisal Abdulrazak,
Aso Mohammad Darwesh,
Asem Khmag,
Baraa T. Shareef
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i6.pp5277-5285
Subject(s) - computer science , artificial intelligence , segmentation , computer vision , pixel , classifier (uml) , path (computing) , point (geometry) , tracking system , image segmentation , tracking (education) , object detection , mathematics , psychology , pedagogy , geometry , programming language , kalman filter
Nowadays, hand gesture recognition (HGR) is getting popular due to several applications such as remote based control using a hand, and security for access control. One of the major problems of HGR is the accuracy lacking hand detection and segmentation. In this paper, a new algorithm of hand detection will be presented, which works by tracking fingers smartly based on the planned path. The tracking operation is accomplished by assuming a point at the top middle of the image containing the object then this point slides few pixels down to be a reference point then branching into two slopes: left and right. On these slopes, fingers will be scanned to extract flip-numbers, which are considered as features to be classified accordingly by utilizing the expert system. Experiments were conducted using 100 images for 10-individual containing hand inside a cluttered background by using Dataset of Leap Motion and Microsoft Kinect hand acquisitions. The recorded accuracy is depended on the complexity of the Flip-Number setting, which is achieved 96%, 84% and 81% in case 6, 7 and 8 Flip_Numbers respectively, in which this result reflects a high level of finite accuracy in comparing with existing techniques.