Hand Posture Recognition Using Adaptive Skin Color Detection and Shape Matching
Date Issued
2011
Date
2011
Author(s)
Chen, Pin-Han
Abstract
Hand gesture recognition has become popular in the field of human-computer interaction research. Because of the convenience and intuitiveness of hand gestures, automobile companies have applied hand gestures in their infotainment systems in order to eliminate driver distractions while on the road.
This thesis proposes a hand gesture recognition method using adaptive skin-color detection for automatic skin color thresholds in order to obtain accurate segmentations of the hand contours, from which similarity of the contour shapes can be calculated. Hand gestures for 10 numbers in Taiwanese sign language are defined and are detected in real-time. Our system can be used for generally static hand gesture recognition applications, or can be extended for dynamic hand gesture recognitions.
The overall average recognition rate for our system is 90%, and our system is more efficient in comparison to previous work while accuracy is unaffected. In addition, the dictionary of our hand gestures is relatively more complete, and users can even use their left hands to get the same satisfactory recognition results.
Subjects
adaptive skin color detection
shape matching
hand posture recognition
Type
thesis
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