Recognition of complex static hand gestures by using the wristband-based contour features

作者：Lee Donq Liang^*; You Wei Shiuan

来源：IET Image Processing, 2018, 12(1): 80-87.

DOI：10.1049/iet-ipr.2016.1139

• gesture recognition
• palmprint recognition
• feature extraction
• image matching
• image segmentation
• object detection
• sharpest corner point detection
• Turkish fingerspelling sign hand gesture recognition
• hand region segmentation
• region merging techniques
• watershed segmentation
• feature matching method
• WBCFs
• degrees of freedom
• wristband-based contour feature extraction
• complex static hand gesture recognition

Recognition of complex static hand gestures is a challenging problem due to the complexity of hand gestures, which are rich in diversities because of high degrees of freedom involved by the human hand. It is more difficult especially when gestures are represented by two hands. This study proposes a framework that can recognise complex static hand gestures by using the wristband-based contour features (WBCFs). The authors require the user to wear a pair of black wristbands on his (her) two hand wrists, so that the hand region(s) can be segmented accurately. The topmost and sharpest corner point of the wristband on a gesturing hand is detected first. It is treated as a landmark to extract the WBCF of a hand gesture. Then, a simple feature matching method is proposed to obtain a recognition result. To deal with the cases where hand region(s) cannot be segmented correctly, watershed segmentation, and region merging techniques are adopted to provide improvements on hand region segmentation. Experimental results show that their system can be used to recognise 29 Turkish fingerspelling sign hand gestures and achieve a recognition accuracy of 99.31% with only six training images for each gesture.

• 出版日期2018-1