Faster R-CNN with Inception V2 for Fingertip Detection in Homogenous Background Image

Muhammad, Fachrurrozi and Derry, Alamsyah (2023) Faster R-CNN with Inception V2 for Fingertip Detection in Homogenous Background Image. Journal of Physics: Conf. Series 1282. ISSN 17426596

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Abstract

Fingertip Detection is a versatile research field in computer vision, since it has multiple purpose such as natural user interface, robotic, 3D simulation etc. It is a challenging research field in computer vision. There are some hand segmentation methods, pre-processing phase, was conducted to provide area of fingertip detection in image. However, in this research, fingertip detection can be done by directly find the fingertip itself. This approach cut off preprocessing phase by using Faster R-CNN method and inception V2 architechture directly to find the fingertip in image. With a homogenous background as a simple input image, this approach showed a good accuracy in its performance. It has 90% and 91% accuracy in way to detect fingertip for both male and female hand datasets. More over, exchanging male and female model toward to male and female dataset gave 94% and 92% accuracy that showed the different pattern between both.

Item Type:	Article
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Dr. Muhammad Fachrurrozi
Date Deposited:	05 Apr 2023 11:48
Last Modified:	05 Apr 2023 11:48
URI:	http://repository.unsri.ac.id/id/eprint/92411

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