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Khaled N. Yasen
Cihan University-Erbil
Computer Science & IT Electrical & Electronics Engineering

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Hand detection and segmentation using smart path tracking fingers as features and expert system classifier Khaled N. Yasen; Fahad Layth Malallah; Lway Faisal Abdulrazak; Aso Mohammad Darwesh; Asem Khmag; Baraa T. Shareef
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (5.602 KB) | DOI: 10.11591/ijece.v9i6.pp5277-5285

Abstract

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.
Co-Authors Asem Khmag Aso Mohammad Darwesh Baraa T. Shareef Fahad Layth Malallah Lway Faisal Abdulrazak