A COMPARISON OF SKIN DETECTION ALGORITHMS FOR HAND GESTURE RECOGNITION

被引:0
|
作者
McBride, Timothy James [1 ]
Vandayar, Nabeel [1 ]
Nixon, Kenneth John [1 ]
机构
[1] Univ Witwatersrand, Sch Elect & Informat Engn, Johannesburg, South Africa
关键词
Human-Computer Interface; Image Processing; Image Segmentation; Skin Detection; Skin Thresholding;
D O I
10.1109/robomech.2019.8704839
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Hand gesture recognition software is becoming more accessible with the advances in depth cameras and sensors, but these sensors are still expensive and not freely available. A real time Hand Gesture Recognition software is designed to work with a low cost monocular web camera. Skin detection and skin extraction is a common form of image processing used for gesture recognition. A comparison of three different skin detection algorithms is performed. The three algorithms are: YCbCr thresholding, RGB-H-CrCb thresholding and KNN Classification. The results obtained for each algorithm show that the algorithms are unreliable with a low mean and a large standard deviation. It was concluded that the uncertainty of the accuracy of each algorithm reduces the effectiveness of the hand gesture recognition software and it is not implemented in the final design. Alternative skin detection algorithms are suggested to improve on the accuracies and latencies obtained.
引用
收藏
页码:211 / 216
页数:6
相关论文
共 50 条
  • [1] Comparison of Algorithms for Dynamic Hand Gesture Recognition
    Kajan, Slavomir
    Goga, Jozef
    Zsiros, Ondrej
    PROCEEDINGS OF THE 2020 30TH INTERNATIONAL CONFERENCE CYBERNETICS & INFORMATICS (K&I '20), 2020,
  • [2] A comparison of machine learning algorithms applied to hand gesture recognition
    Trigueiros, Paulo
    Ribeiro, Fernando
    Reis, Luis Paulo
    SISTEMAS Y TECNOLOGIAS DE INFORMACION, VOLS 1 AND 2, 2012, : 41 - +
  • [3] A comparison of machine learning algorithms applied to hand gesture recognition
    Trigueiros, Paulo
    Ribeiro, Fernando
    Reis, Luis Paulo
    7TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI 2012), 2012,
  • [4] FAST HAND DETECTION AND GESTURE RECOGNITION
    Wang, Yuh-Rau
    Syu, Jia-Liang
    Li, Hsin-Ting
    Yang, Ling
    PROCEEDINGS OF 2015 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL. 1, 2015, : 408 - 413
  • [5] Comparison of Hand Segmentation Methodologies for Hand Gesture Recognition
    Howe, Lim Wei
    Wong, Farrah
    Chekima, Ali
    INTERNATIONAL SYMPOSIUM OF INFORMATION TECHNOLOGY 2008, VOLS 1-4, PROCEEDINGS: COGNITIVE INFORMATICS: BRIDGING NATURAL AND ARTIFICIAL KNOWLEDGE, 2008, : 914 - 920
  • [6] Skin Cluster Tracking and Verification for Hand Gesture Recognition
    Gutzeit, Enrico
    Vahl, Matthias
    Zhou, Zhiliang
    von Lukas, Uwe
    PROCEEDINGS OF THE 7TH INTERNATIONAL SYMPOSIUM ON IMAGE AND SIGNAL PROCESSING AND ANALYSIS (ISPA 2011), 2011, : 241 - 246
  • [7] Hand gesture recognition based on fingertip detection
    Meng, Guoqing
    Wang, Mei
    2013 FOURTH GLOBAL CONGRESS ON INTELLIGENT SYSTEMS (GCIS), 2013, : 107 - 111
  • [8] Real Time Hand Gesture Recognition by Skin Color Detection for American Sign Language
    Khan, Shomi
    Ali, M. Elieas
    Das, Sree Sourav
    Rahman, Md Mohsinur
    2019 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL INFORMATION AND COMMUNICATION TECHNOLOGY (EICT), 2019,
  • [9] Evaluation of HMM training algorithms for letter hand gesture recognition
    Liu, N
    Lovell, BC
    Kootsookos, PJ
    PROCEEDINGS OF THE 3RD IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY, 2003, : 648 - 651
  • [10] Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey
    Jiang, Shuo
    Kang, Peiqi
    Song, Xinyu
    Lo, Benny P. L.
    Shull, Peter B.
    IEEE REVIEWS IN BIOMEDICAL ENGINEERING, 2022, 15 : 85 - 102