Local binary pattern based features for sign language recognition

被引：4
|
作者：
Hrúz M. [1 ]
Trojanová J. [1 ]
Železný M. [1 ]
机构：
[1] Faculty of Applied Sciences, University of West Bohemia, Pilsen
来源：
Pattern Recognition and Image Analysis | 2011年 / 21卷 / 3期
关键词：
Recognition Rate; Local Binary Pattern; Facial Landmark; Geometric Moment; Local Binary Pattern Feature;
D O I：
10.1134/S1054661811020416
中图分类号：
学科分类号：
摘要：
In this paper we focus on appearance features describing the manual component of Sign Language particularly the Local Binary Patterns. We compare the performance of these features with geometric moments describing the trajectory and shape of hands. Since the non-manual component is also very important for sign recognition we localize facial landmarks via Active Shape Model combined with Landmark detector that increases the robustness of model fitting. We test the recognition performance of individual features and their combinations on a database consisting of 11 signers and 23 signs with several repetitions. Local Binary Patterns outperform the geometric moments. When the features are combined we achieve a recognition rate up to 99. 75% for signer dependent tests and 57. 54% for signer independent tests. © 2011 Pleiades Publishing, Ltd.
引用
下载
收藏
页码：398 / 401
页数：3
相关论文
共 50 条
  • [1] Local Binary Pattern based features for sign language recognition
    M. Hrúz
    J. Trojanová
    M. Železný
    Pattern Recognition and Image Analysis, 2012, 22 (4) : 519 - 526
  • [2] Recognition of local features for camera-based sign language recognition system
    Imagawa, K
    Matsuo, H
    Taniguchi, R
    Arita, D
    Lu, S
    Igi, S
    15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 4, PROCEEDINGS: APPLICATIONS, ROBOTICS SYSTEMS AND ARCHITECTURES, 2000, : 849 - 853
  • [3] Indian sign language recognition using Krawtchouk moment-based local features
    Kaur, Bineet
    Joshi, Garima
    Vig, Renu
    IMAGING SCIENCE JOURNAL, 2017, 65 (03): : 171 - 179
  • [4] USING COLOUR LOCAL BINARY PATTERN FEATURES FOR FACE RECOGNITION
    Choi, Jae Young
    Plataniotis, Konstantinos N.
    Ro, Yong Man
    2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, 2010, : 4541 - 4544
  • [5] FACIAL EXPRESSION RECOGNITION BASED ON LOCAL BINARY PATTERN FEATURES AND SUPPORT VECTOR MACHINE
    Nhan Thi Cao
    An Hoa Ton-That
    Hyung Il Choi
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2014, 28 (06)
  • [6] Face recognition based on curvelets and local binary pattern features via using local property preservation
    Zhou, Lijian
    Liu, Wanquan
    Lu, Zhe-ming
    Nie, Tingyuan
    JOURNAL OF SYSTEMS AND SOFTWARE, 2014, 95 : 209 - 216
  • [7] Woods Recognition System Based on Local Binary Pattern
    Nasirzadeh, M.
    Khazael, A. Arab
    bin Khalid, Marzuki
    2010 SECOND INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE, COMMUNICATION SYSTEMS AND NETWORKS (CICSYN), 2010, : 308 - 313
  • [8] LOCAL BINARY PATTERN ORIENTATION BASED FACE RECOGNITION
    Shen, Yi-Kang
    Chiu, Ching-Te
    2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP), 2015, : 1091 - 1095
  • [9] Smoke Image Recognition Based on Local Binary pattern
    Tang, Tiantian
    Dai, Linhan
    Yin, Zhijian
    PROCEEDINGS OF THE 2017 5TH INTERNATIONAL CONFERENCE ON MECHATRONICS, MATERIALS, CHEMISTRY AND COMPUTER ENGINEERING (ICMMCCE 2017), 2017, 141 : 1118 - 1123
  • [10] Facial Expression Recognition by Fusing Gabor and Local Binary Pattern Features
    Sun, Yuechuan
    Yu, Jun
    MULTIMEDIA MODELING, MMM 2017, PT II, 2017, 10133 : 209 - 220
12345