DocumentCode :
1672229
Title :
Head gestures recognition
Author :
Ng, Pei Chi ; De Silva, Liyanage C.
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore
Volume :
3
fYear :
2001
fDate :
6/23/1905 12:00:00 AM
Firstpage :
266
Abstract :
We describe a system that automatically detects and recognizes human head gestures such as nodding and shaking in complex background conditions using a cheap Web camera under uncontrolled conditions. The images of the head, captured at 20 frames per second, are very noisy and are of a low resolution. The invariant moments of each image captured is extracted and is fed into a recognition system that uses discrete hidden Markov models (HMMs) to classify the head gestures. The system achieves an average success rate of 87%. The system can successfully run on any low to high end PC connected to a USB Web camera without any manual initialization
Keywords :
gesture recognition; hidden Markov models; image classification; image resolution; image sequences; object detection; HMM; USB Web cam; Web camera; discrete hidden Markov models; gesture classification; head gesture detection; human head gesture recognition; nodding; shaking; Application software; Education; Facial features; Head; Hidden Markov models; Humans; Image resolution; Positron emission tomography; Robots; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2001. Proceedings. 2001 International Conference on
Conference_Location :
Thessaloniki
Print_ISBN :
0-7803-6725-1
Type :
conf
DOI :
10.1109/ICIP.2001.958102
Filename :
958102
Link To Document :
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